AllResultsSimulationExperiment.rtf

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\deftab720
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__________________________________________________\cf0 \
\pard\pardeftab720\partightenfactor0

\f1\fs26\fsmilli13067 \cf4 \cb5 ## Configuration stands for correspondingly s1, s2, nns, recombination rate,\cb1 \
\cb5 ### ploidy (1 = haploid_1000, 2 = diploid_1000, 3=yeast, 4=haploid_500, 5=diploid_500), no_replicates. \cb1 \
\pard\pardeftab720\partightenfactor0

\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 \cf4 \
\pard\pardeftab720\partightenfactor0

\f0\fs22 \AppleTypeServices \cf2 __________________________________________________\cf0 \
\cf2 Config 0: (Avg. runtime: Missing, but average 15,000 second)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.700438],[0.19150808],[0.10805392]). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([1.],0,0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.93610557],[0.06389443],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([1.],0,0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([1.],0,0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.93909247],[0.06090753],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.96296496],[0.03703504],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.98559817],[0.01440183],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.93804469],[0.06195531],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.95076045],[0.04923955],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.84049701],0,[0.15950299]). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.93542721],[0.06457279],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.97681503],[0.02318497],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.95082709],[0.04917291],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.93934991],[0.06065009],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.97629857],[0.02370143],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.92619126],[0.07380874],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.86044957],[0.00986812],[0.12968231]). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.95692564],[0.04307436],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.98830181],[0.01169819],0). Inferred nns: 0.0\cf0 \
\cf2 True s1: 0.0, True s2: 0.0, True nns: 0, True rec rate: 0.0, hap/dip-loid: 1.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0. 0.]\cf0 \
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\cf2 _________________________\cf0 \
\cf2 Config 1: (Avg. runtime: 13,454.4)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.4677236],[0.44102781],[0.0912486]). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.2803573],[0.56859415],[0.15104855]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01368815 0.        ]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.59510258],[0.40489742]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0196228 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.67453989],[0.32546011]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01723614 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.76931825],[0.23068175]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0191411 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.79157225],[0.20842775]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01680756 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.01165245],[0.7771086],[0.21123895]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01828505 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.09683],[0.73738573],[0.16578427]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0160798 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.70185834],[0.29814166]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02415894 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.75861582],[0.24138418]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01749427 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.45647822],[0.49719229],[0.04632949]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01353384 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.10877868],[0.60741358],[0.28380774]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01964592 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.90018269],[0.09981731],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.8554595],[0.1445405]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01504548 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.52724562],[0.41484688],[0.0579075]). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.75849419],[0.24150581]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01493492 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.74279391],[0.25720609]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02505198 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.85992074],[0.14007926]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0160281 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.27762925],[0.59074818],[0.13162258]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01530495 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.70287648],[0.29712352]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02314838 0.        ]\cf0 \
\cf2 True s1: 0.02, True s2: 0.0, True nns: 1, True rec rate: 0.0, hap/dip-loid: 1.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 0. 1. 1. 1. 1. 1.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.         0.        ]\cf0 \
\cf2  [0.01368815 0.        ]\cf0 \
\cf2  [0.0196228  0.        ]\cf0 \
\cf2  [0.01723614 0.        ]\cf0 \
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\cf2  [0.01680756 0.        ]\cf0 \
\cf2  [0.01828505 0.        ]\cf0 \
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\cf2  [0.02415894 0.        ]\cf0 \
\cf2  [0.01749427 0.        ]\cf0 \
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\cf2  [0.01964592 0.        ]\cf0 \
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\cf2  [0.02505198 0.        ]\cf0 \
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\cf2  [0.01530495 0.        ]\cf0 \
\cf2  [0.02314838 0.        ]]\cf0 \
\
\cf2 Runtime: (11528+10659+8943+14258+11598+21030+12664+18672+14636+10556)/10=13,454.4 \cf0 \
\cf2 Inference now running for 10-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 11528.949567079544 seconds\cf0 \
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 10659.559743881226 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 8943.141436576843 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 14258.70460319519 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 11598.65063738823 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 21030.665584087372 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 12664.843730211258 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 18672.15573143959 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 14636.564580440521 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.02, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
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\cf2 Inference completed in 10556.278007268906 seconds\cf0 \
\cf2 _________________________\cf0 \
\cf2 Config 2: (Avg. runtime: 16,384.6)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.69195405],[0.30804595]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03991222 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.77927128],[0.22072872]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06416808 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.72294367],[0.27705633]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05296691 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.62722703],[0.37277297]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06139319 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.05357138],[0.80284462],[0.143584]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01886052 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.63979736],[0.3136684],[0.04653424]). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.69808091],[0.30191909]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05287162 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.75981619],[0.24018381]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01828416 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.70530287],[0.29469713]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03289295 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.28897939],[0.71102061]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04119407 0.03955493]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.54175078],[0.45824922]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04181767 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.67542362],[0.32457638]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04164699 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.78240189],[0.21759811]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04362331 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.74280194],[0.25719806]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04668277 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.67409405],[0.32590595]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04976289 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.32241519],[0.53397411],[0.14361069]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04072782 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.59319411],[0.40680589]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05227157 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.69189306],[0.30810694]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05072322 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.75717751],[0.24282249]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04175176 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.33743475],[0.60700291],[0.05556234]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03913345 0.        ]\cf0 \
\cf2 True s1: 0.05, True s2: 0.0, True nns: 1, True rec rate: 0.0, hap/dip-loid: 1.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [1. 1. 1. 1. 1. 0. 1. 1. 1. 2. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.03991222 0.        ]\cf0 \
\cf2  [0.06416808 0.        ]\cf0 \
\cf2  [0.05296691 0.        ]\cf0 \
\cf2  [0.06139319 0.        ]\cf0 \
\cf2  [0.01886052 0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.05287162 0.        ]\cf0 \
\cf2  [0.01828416 0.        ]\cf0 \
\cf2  [0.03289295 0.        ]\cf0 \
\cf2  [0.04119407 0.03955493]\cf0 \
\cf2  [0.04181767 0.        ]\cf0 \
\cf2  [0.04164699 0.        ]\cf0 \
\cf2  [0.04362331 0.        ]\cf0 \
\cf2  [0.04668277 0.        ]\cf0 \
\cf2  [0.04976289 0.        ]\cf0 \
\cf2  [0.04072782 0.        ]\cf0 \
\cf2  [0.05227157 0.        ]\cf0 \
\cf2  [0.05072322 0.        ]\cf0 \
\cf2  [0.04175176 0.        ]\cf0 \
\
\cf2  [0.03913345 0.        ]]\cf0 \
\
\cf2 Runtime: (11538+11248+16202+24420+27727+15574+11556+22032+13110+10439)/10=16,384.6 \cf0 \
\cf2 Inference now running for 10-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 11538.032838582993 seconds\cf0 \
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 11248.606883049011 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 16202.664233207703 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 24420.67117357254 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 27727.195769309998 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 15574.050758600235 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 11556.953183889389 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 22032.63802099228 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 13110.360317707062 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.05, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\
\cf2 Inference completed in 10439.145028591156 seconds\cf0 \
\cf2 _________________________\cf0 \
\cf2 Config 3: (Avg. runtime: 13,884.7)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.48273718],[0.51726282]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02754608 0.07683097]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.73605796],[0.26394204]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07387994 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.70848978],[0.29151022]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07421338 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.44218451],[0.55781549]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06100725 0.06252826]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.71693287],[0.28306713]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03990204 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.64781997],[0.35218003]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07149071 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.65145303],[0.34854697]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05796167 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.72048291],[0.27951709]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03735831 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.66643437],[0.33356563]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07726907 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.83545323],[0.16454677]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07727926 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.58850719],[0.41149281]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06709595 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.55869141],[0.44130859]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0563437 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.01655786],[0.80961438],[0.17382776]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03920773 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.79624482],[0.20375518]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05892242 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.69564999],[0.30435001]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0795737 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.65346421],[0.34653579]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06451011 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.5702798],[0.4297202]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07281183 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.56567313],[0.43432687]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07224657 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.02216505],[0.75184186],[0.22599309]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04355935 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.61562868],[0.38437132]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05772268 0.        ]\cf0 \
\cf2 True s1: 0.07, True s2: 0.0, True nns: 1, True rec rate: 0.0, hap/dip-loid: 1.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [2. 1. 1. 2. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.02754608 0.07683097]\cf0 \
\cf2  [0.07387994 0.        ]\cf0 \
\cf2  [0.07421338 0.        ]\cf0 \
\cf2  [0.06100725 0.06252826]\cf0 \
\cf2  [0.03990204 0.        ]\cf0 \
\cf2  [0.07149071 0.        ]\cf0 \
\cf2  [0.05796167 0.        ]\cf0 \
\cf2  [0.03735831 0.        ]\cf0 \
\cf2  [0.07726907 0.        ]\cf0 \
\cf2  [0.07727926 0.        ]\cf0 \
\cf2  [0.06709595 0.        ]\cf0 \
\cf2  [0.0563437  0.        ]\cf0 \
\cf2  [0.03920773 0.        ]\cf0 \
\cf2  [0.05892242 0.        ]\cf0 \
\cf2  [0.0795737  0.        ]\cf0 \
\cf2  [0.06451011 0.        ]\cf0 \
\cf2  [0.07281183 0.        ]\cf0 \
\cf2  [0.07224657 0.        ]\cf0 \
\cf2  [0.04355935 0.        ]\cf0 \
\cf2  [0.05772268 0.        ]]\cf0 \
\
\cf2 Runtime: (14430+20139+9576+10758+11927+15311+14445+10377+15029+16855)/10=13,884.7 \cf0 \
\cf2 Inference now running for 10-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 14430.862497091293 seconds\cf0 \
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 20139.265265226364 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 9576.145023107529 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 10758.66426539421 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 11927.351072788239 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 15311.147096633911 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 14445.762083530426 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 10377.470121145248 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 15029.136837244034 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 1.0, 20.0]\cf0 \
\
\cf2 Inference completed in 16855.327113628387 seconds\cf0 \
\cf2 _________________________\cf0 \
\cf2 Config 4: (Avg. runtime: 13,626.7)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.62313882],[0.37686118]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03626295 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.58645335],[0.41354665]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02707294 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.68747086],[0.31252914]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01952055 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.36509103],[0.63490897]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05896745 0.05991647]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.83042051],[0.16957949]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03794508 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.69023118],[0.30976882]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04324185 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.79054917],[0.20945083]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03307468 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.75036378],[0.24963622]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06977026 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.6594119],[0.3405881]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02411704 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.67613341],[0.32386659]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05411163 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.64589057],[0.35410943]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03979103 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.5783632],[0.4216368]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03349065 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.70741562],[0.29258438]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0232153 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.6144954],[0.3855046]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07262453 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.47594878],[0.52405122]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08168739 0.02141793]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.66426208],[0.33573792]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02104003 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.66357926],[0.33642074]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03655238 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.65968195],[0.34031805]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04169707 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.57004758],[0.42995242]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09040848 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.70257774],[0.29742226]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02530622 0.        ]\cf0 \
\cf2 True s1: 0.05, True s2: 0.02, True nns: 2, True rec rate: 0.0, hap/dip-loid: 1.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [1. 1. 1. 2. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 2. 1. 1. 1. 1. 1.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.03626295 0.        ]\cf0 \
\cf2  [0.02707294 0.        ]\cf0 \
\cf2  [0.01952055 0.        ]\cf0 \
\cf2  [0.05896745 0.05991647]\cf0 \
\cf2  [0.03794508 0.        ]\cf0 \
\cf2  [0.04324185 0.        ]\cf0 \
\cf2  [0.03307468 0.        ]\cf0 \
\cf2  [0.06977026 0.        ]\cf0 \
\cf2  [0.02411704 0.        ]\cf0 \
\cf2  [0.05411163 0.        ]\cf0 \
\cf2  [0.03979103 0.        ]\cf0 \
\cf2  [0.03349065 0.        ]\cf0 \
\cf2  [0.0232153  0.        ]\cf0 \
\cf2  [0.07262453 0.        ]\cf0 \
\cf2  [0.08168739 0.02141793]\cf0 \
\cf2  [0.02104003 0.        ]\cf0 \
\cf2  [0.03655238 0.        ]\cf0 \
\cf2  [0.04169707 0.        ]\cf0 \
\cf2  [0.09040848 0.        ]\cf0 \
\cf2  [0.02530622 0.        ]]\cf0 \
\
\cf2 Runtime: (6993+15481+13309+20388+18093+11630+13807+18606+11038+6922)/10=13,626.7 \cf0 \
\
\cf2 Inference now running for 10-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 6993.134213209152 seconds\cf0 \
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 15481.24206495285 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 13309.821538209915 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 20388.94904923439 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 18093.03662633896 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 11630.258253097534 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 13807.0341360569 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 18606.421221971512 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 11038.355974435806 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 6922.19408082962 seconds\cf0 \
\pard\pardeftab720\partightenfactor0

\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 \cf2 \
\
\pard\pardeftab720\partightenfactor0

\f0\fs22 \AppleTypeServices \cf2 _________________________\cf0 \
\cf2 Cpnfig 5: (Avg. runtime: 14,066.0909)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.59615361],[0.40384639]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.11831268 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.32334761],[0.67665239]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06316959 0.06056413]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.43179186],[0.56820814]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02719482 0.05553161]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.31340257],[0.68659743]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0219106 0.0568793]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.68058784],[0.31941216]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06090785 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.27634786],[0.72365214]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09448944 0.07583569]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.35765652],[0.64234348]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05337843 0.04142844]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.07826897],[0.92173103]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.14773138 0.09592225]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.51000259],[0.48999741]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09081448 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.57435134],[0.42564866]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09275221 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.54112525],[0.45887475]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10749177 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.36593251],[0.63406749]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08584135 0.09653674]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.63324232],[0.36675768]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07132333 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.84800465],[0.0232216],[0.12877376]). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.60614444],[0.39385556]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.13008721 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.59543228],[0.40456772]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07175389 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.6393455],[0.3606545]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07710807 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.22240437],[0.77759563]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08417533 0.07134505]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.61622564],[0.38377436]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06088321 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.59453343],[0.40546657]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.12580706 0.        ]\cf0 \
\cf2 True s1: 0.05, True s2: 0.07, True nns: 2, True rec rate: 0.0, hap/dip-loid: 1.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [1. 2. 2. 2. 1. 2. 2. 2. 1. 1. 1. 2. 1. 0. 1. 1. 1. 2. 1. 1.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.11831268 0.        ]\cf0 \
\cf2  [0.06316959 0.06056413]\cf0 \
\cf2  [0.02719482 0.05553161]\cf0 \
\cf2  [0.0219106  0.0568793 ]\cf0 \
\cf2  [0.06090785 0.        ]\cf0 \
\cf2  [0.09448944 0.07583569]\cf0 \
\cf2  [0.05337843 0.04142844]\cf0 \
\cf2  [0.14773138 0.09592225]\cf0 \
\cf2  [0.09081448 0.        ]\cf0 \
\cf2  [0.09275221 0.        ]\cf0 \
\cf2  [0.10749177 0.        ]\cf0 \
\cf2  [0.08584135 0.09653674]\cf0 \
\cf2  [0.07132333 0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.13008721 0.        ]\cf0 \
\cf2  [0.07175389 0.        ]\cf0 \
\cf2  [0.07710807 0.        ]\cf0 \
\cf2  [0.08417533 0.07134505]\cf0 \
\cf2  [0.06088321 0.        ]\cf0 \
\cf2  [0.12580706 0.        ]]\cf0 \
\
\cf2 Runtime: (11565+10925+14070+17654+9468+12174+14545+18772+20298+13371+11885)/11=14,066.0909 \cf0 \
\cf2 Inference now running for 9-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 11565.360728740692 seconds\cf0 \
\cf2 Inference now running for 10-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 10925.725972175598 seconds\cf0 \
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 14070.311942338943 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 17654.381722927094 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 9468.505710363388 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 12174.73270869255 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 14545.195847034454 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 18772.23673081398 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 20298.03160429001 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 13371.206775188446 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 1.0, 20.0]\cf0 \
\
\cf2 Inference completed in 11885.185774326324 seconds\cf0 \
\cf2 _________________________
\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 \
Config 6:  
\f0\fs22 \AppleTypeServices (Avg. runtime: 14412)
\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 \

\f0\fs22 \AppleTypeServices Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.33552471],[0.66447529]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.16183128 0.05433134]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.16940156],[0.83059844]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08396317 0.10041564]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.61786369],[0.38213631]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09287889 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.42367055],[0.57632945]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.14034489 0.02697317]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.65681562],[0.34318438]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09470974 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.31565073],[0.68434927]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07219748 0.08194399]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.17575679],[0.82424321]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08486247 0.08655565]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.53914673],[0.46085327]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08273909 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.37640463],[0.62359537]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06773168 0.06356492]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.26585922],[0.73414078]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06729722 0.07152602]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.54448026],[0.45551974]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06190724 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.5424455],[0.4575545]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06983904 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.06762743],[0.71595448],[0.21641809]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01635779 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.23434421],[0.76565579]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09883192 0.11318405]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.63154235],[0.36845765]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09384612 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.58421867],[0.41578133]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09829881 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.38537723],[0.61462277]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07390313 0.06311371]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.64222514],[0.35777486]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09405666 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.41888777],[0.58111223]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.15500646 0.02909799]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.3120568],[0.6879432]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07522195 0.07339275]\cf0 \
\cf2 True s1: 0.07, True s2: 0.09, True nns: 2, True rec rate: 0.0, hap/dip-loid: 1.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [2. 2. 1. 2. 1. 2. 2. 1. 2. 2. 1. 1. 1. 2. 1. 1. 2. 1. 2. 2.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.16183128 0.05433134]\cf0 \
\cf2  [0.08396317 0.10041564]\cf0 \
\cf2  [0.09287889 0.        ]\cf0 \
\cf2  [0.14034489 0.02697317]\cf0 \
\cf2  [0.09470974 0.        ]\cf0 \
\cf2  [0.07219748 0.08194399]\cf0 \
\cf2  [0.08486247 0.08655565]\cf0 \
\cf2  [0.08273909 0.        ]\cf0 \
\cf2  [0.06773168 0.06356492]\cf0 \
\cf2  [0.06729722 0.07152602]\cf0 \
\cf2  [0.06190724 0.        ]\cf0 \
\cf2  [0.06983904 0.        ]\cf0 \
\cf2  [0.01635779 0.        ]\cf0 \
\cf2  [0.09883192 0.11318405]\cf0 \
\cf2  [0.09384612 0.        ]\cf0 \
\cf2  [0.09829881 0.        ]\cf0 \
\cf2  [0.07390313 0.06311371]\cf0 \
\cf2  [0.09405666 0.        ]\cf0 \
\cf2  [0.15500646 0.02909799]\cf0 \
\cf2  [0.07522195 0.07339275]]\cf0 \
\
\cf2 Runtime: (11911+22499+13631+9372+13684+12337+15241+18295+17286+16867+7417)/11=14412\cf0 \
\cf2 Inference now running for 9-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 11911.773373126984 seconds\cf0 \
\cf2 Inference now running for 10-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 22499.220893383026 seconds\cf0 \
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 13631.988931655884 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 9372.054468393326 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 13684.673771858215 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 12337.54382967949 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 15241.782766580582 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 18295.67093014717 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 17286.484800815582 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\cf2 Inference completed in 16867.58285498619 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 20.0]\cf0 \
\
\cf2 Inference completed in 7417.393377542496 seconds\cf0 \
\pard\pardeftab720\partightenfactor0

\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 \cf2 \
\pard\pardeftab720\partightenfactor0

\f0\fs22 \AppleTypeServices \cf2 _________________________
\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 \
Config 7:  
\f0\fs22 \AppleTypeServices (Avg. runtime: 16,422.5)
\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 \

\f0\fs22 \AppleTypeServices Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.54373047],[0.45626953]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07100947 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.53233557],[0.46766443]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08263501 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.73205591],[0.26794409]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09243278 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.30251857],[0.69748143]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02787206 0.13600134]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.51096443],[0.48903557]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10114861 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.55988932],[0.44011068]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.16299175 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.4097432],[0.5902568]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08401469 0.02347803]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.29654289],[0.70345711]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10001712 0.09138208]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.49657243],[0.50342757]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01943915 0.06114959]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.66718908],[0.33281092]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10492569 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.39003393],[0.60996607]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.055036   0.04752614]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.5832295],[0.4167705]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07384614 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.70088356],[0.29911644]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.11261295 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.50614827],[0.49385173]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10310036 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.59960865],[0.40039135]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08999598 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.52950803],[0.47049197]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08185041 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.5637461],[0.4362539]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10077483 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.80275035],[0.19724965]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.11456787 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.60712493],[0.39287507]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.11946143 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.38355958],[0.61644042]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04263068 0.17756152]\cf0 \
\cf2 True s1: 0.07, True s2: 0.09, True nns: 2, True rec rate: 0.0, hap/dip-loid: 1.0, number of replicates: 5.0\cf0 \
\cf2 Inferred nns: [1. 1. 1. 2. 1. 1. 2. 2. 2. 1. 2. 1. 1. 1. 1. 1. 1. 1. 1. 2.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.07100947 0.        ]\cf0 \
\cf2  [0.08263501 0.        ]\cf0 \
\cf2  [0.09243278 0.        ]\cf0 \
\cf2  [0.02787206 0.13600134]\cf0 \
\cf2  [0.10114861 0.        ]\cf0 \
\cf2  [0.16299175 0.        ]\cf0 \
\cf2  [0.08401469 0.02347803]\cf0 \
\cf2  [0.10001712 0.09138208]\cf0 \
\cf2  [0.01943915 0.06114959]\cf0 \
\cf2  [0.10492569 0.        ]\cf0 \
\cf2  [0.055036   0.04752614]\cf0 \
\cf2  [0.07384614 0.        ]\cf0 \
\cf2  [0.11261295 0.        ]\cf0 \
\cf2  [0.10310036 0.        ]\cf0 \
\cf2  [0.08999598 0.        ]\cf0 \
\cf2  [0.08185041 0.        ]\cf0 \
\cf2  [0.10077483 0.        ]\cf0 \
\cf2  [0.11456787 0.        ]\cf0 \
\cf2  [0.11946143 0.        ]\cf0 \
\cf2  [0.04263068 0.17756152]]\cf0 \
\
\cf2 Runtime: (11018+21018+21967+22904+16351+16176+21078+10812+9889+13012)/10=16,422.5 \cf0 \
\
\cf2 Inference now running for 10-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 Inference completed in 11018.175228834152 seconds\cf0 \
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 Inference completed in 21018.50289940834 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 Inference completed in 21967.658848762512 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 Inference completed in 22904.0688290596 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 Inference completed in 16351.31185889244 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 Inference completed in 16176.41885638237 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 Inference completed in 21078.56308865547 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 Inference completed in 10812.110044240952 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 Inference completed in 9889.896895885468 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 5.0]\cf0 \
\cf2 Inference completed in 13012.451829910278 seconds\cf0 \
\pard\pardeftab720\partightenfactor0

\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 \cf2 \
\pard\pardeftab720\partightenfactor0

\f0\fs22 \AppleTypeServices \cf2 _________________________
\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 \
Config 8: 
\f0\fs22 \AppleTypeServices (Avg. runtime: 14,553.9)
\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 \

\f0\fs22 \AppleTypeServices Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.49760235],[0.50239765]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09616699 0.02213413]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.36124868],[0.63875132]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07121141 0.16343786]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.50466431],[0.49533569]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.11548894 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.64476854],[0.35523146]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09446843 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.37210117],[0.62789883]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08180068 0.09073438]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.5479261],[0.4520739]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.1019608 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.36575965],[0.63424035]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06655306 0.05504822]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.52369387],[0.47630613]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07769173 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.32208469],[0.67791531]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.17246506 0.03288111]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.30491766],[0.69508234]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09147082 0.09574194]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.70399903],[0.29600097]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08060516 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.27549335],[0.72450665]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10095589 0.0123282 ]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.25698965],[0.74301035]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03988569 0.07174047]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.57456242],[0.42543758]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.12479883 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.30749116],[0.69250884]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03188795 0.07048355]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.41439923],[0.58560077]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07705558 0.07225867]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.64684152],[0.35315848]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10596344 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.66578942],[0.33421058]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.11079189 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.50458886],[0.49541114]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.17681415 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.48900497],[0.51099503]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.14569701 0.03469148]\cf0 \
\cf2 True s1: 0.07, True s2: 0.09, True nns: 2, True rec rate: 0.0, hap/dip-loid: 1.0, number of replicates: 10.0\cf0 \
\cf2 Inferred nns: [2. 2. 1. 1. 2. 1. 2. 1. 2. 2. 1. 2. 2. 1. 2. 2. 1. 1. 1. 2.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.09616699 0.02213413]\cf0 \
\cf2  [0.07121141 0.16343786]\cf0 \
\cf2  [0.11548894 0.        ]\cf0 \
\cf2  [0.09446843 0.        ]\cf0 \
\cf2  [0.08180068 0.09073438]\cf0 \
\cf2  [0.1019608  0.        ]\cf0 \
\cf2  [0.06655306 0.05504822]\cf0 \
\cf2  [0.07769173 0.        ]\cf0 \
\cf2  [0.17246506 0.03288111]\cf0 \
\cf2  [0.09147082 0.09574194]\cf0 \
\cf2  [0.08060516 0.        ]\cf0 \
\cf2  [0.10095589 0.0123282 ]\cf0 \
\cf2  [0.03988569 0.07174047]\cf0 \
\cf2  [0.12479883 0.        ]\cf0 \
\cf2  [0.03188795 0.07048355]\cf0 \
\cf2  [0.07705558 0.07225867]\cf0 \
\cf2  [0.10596344 0.        ]\cf0 \
\cf2  [0.11079189 0.        ]\cf0 \
\cf2  [0.17681415 0.        ]\cf0 \
\cf2  [0.14569701 0.03469148]]\cf0 \
\
\cf2 Runtime: (12162+19495+19208+14456+10334+13872+14175+15152+14798+11887)/10=14,553.9 \cf0 \
\cf2 Inference now running for 10-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 Inference completed in 12162.297696113586 seconds\cf0 \
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 Inference completed in 19495.171577692032 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 Inference completed in 19208.611593961716 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 Inference completed in 14456.903369665146 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 Inference completed in 10334.316274881363 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 Inference completed in 13872.173103809357 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 Inference completed in 14175.434895992279 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 Inference completed in 15152.1474173069 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\cf2 Inference completed in 14798.251121520996 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 1.0, 10.0]\cf0 \
\
\cf2 Inference completed in 11887.073937654495 seconds\cf0 \
\cf2 _________________________\cf0 \
\cf2 Config 9: (Avg. runtime: 
\f2\fs24 15,248.909
\f0\fs22 )\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.98824386],[0.01175614],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.98701888],[0.01298112],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.97223691],[0.02776309],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.9732907],[0.0267093],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.95516821],[0.04483179],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([1.],0,0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([1.],0,0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.98751476],[0.01248524],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.97526696],[0.02473304],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.97589814],[0.02410186],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.96952838],[0.03047162],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.96485986],[0.03514014],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([1.],0,0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([1.],0,0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.98868549],[0.01131451],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.95576486],[0.04423514],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.98005879],[0.01994121],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.98775312],[0.01224688],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([1.],0,0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.98864424],[0.01135576],0). Inferred nns: 0.0\cf0 \
\cf2 True s1: 0.0, True s2: 0.0, True nns: 0, True rec rate: 0.0, hap/dip-loid: 2.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0. 0.]\cf0 \
\cf2  [0. 0.]\cf0 \
\cf2  [0. 0.]\cf0 \
\cf2  [0. 0.]\cf0 \
\cf2  [0. 0.]\cf0 \
\cf2  [0. 0.]\cf0 \
\cf2  [0. 0.]\cf0 \
\cf2  [0. 0.]\cf0 \
\cf2  [0. 0.]\cf0 \
\cf2  [0. 0.]\cf0 \
\cf2  [0. 0.]\cf0 \
\cf2  [0. 0.]\cf0 \
\cf2  [0. 0.]\cf0 \
\cf2  [0. 0.]\cf0 \
\cf2  [0. 0.]\cf0 \
\cf2  [0. 0.]\cf0 \
\cf2  [0. 0.]\cf0 \
\cf2  [0. 0.]\cf0 \
\cf2  [0. 0.]\cf0 \
\cf2  [0. 0.]]\cf0 \
\pard\pardeftab720\partightenfactor0

\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 \cf2 \
\pard\pardeftab720\partightenfactor0

\f0\fs22 \AppleTypeServices \cf2 Runtime: 
\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 (11951+16339+18229+23749+14324+20907+6111+13580+14060+13272+15216)/11=15,248.909 \

\f0\fs22 \AppleTypeServices Inference now running for 0-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 11951.43920326233 seconds\cf0 \
\cf2 Inference now running for 1-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 16339.765409946442 seconds\cf0 \
\cf2 Inference now running for 2-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 18229.34209537506 seconds\cf0 \
\cf2 Inference now running for 3-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 23749.67364859581 seconds\cf0 \
\cf2 Inference now running for 4-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 14324.078586101532 seconds\cf0 \
\cf2 Inference now running for 5-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\
\cf2 Inference completed in 20907.441599845886 seconds\cf0 \
\
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 6111.755352258682 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 13580.875341176987 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 14060.406220674515 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 13272.999403715134 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.0, 0.0, 0, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 15216.889388561249 seconds\cf0 \
\pard\pardeftab720\partightenfactor0

\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 \cf2 \
\pard\pardeftab720\partightenfactor0

\f0\fs22 \AppleTypeServices \cf2 _________________________\cf0 \
\cf2 Config 10: (Avg. runtime: 14,637.889)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.55936717],[0.44063283]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02027206 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.70185249],[0.29814751]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02273191 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.93848652],[0.06151348],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.80305393],[0.19694607]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0277615 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.77901887],[0.22098113]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03670285 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.81131879],[0.18868121]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03698767 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.95709766],[0.04290234],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.93512539],[0.06487461],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.71931907],[0.28068093]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03465843 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.75982031],[0.24017969]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03148729 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.72704681],[0.27295319]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0300761 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.88242923],[0.11757077]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02666408 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.83911985],[0.16088015]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03738501 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.82614466],[0.17385534]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02851382 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.48577352],[0.51422648]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03404527 0.02483235]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.96442894],[0.03557106],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.816852],[0.183148]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02598348 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.96085457],[0.03914543],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.95258449],[0.04741551],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.82117429],[0.17882571]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02760007 0.        ]\cf0 \
\cf2 True s1: 0.07, True s2: 0.0, True nns: 1, True rec rate: 0.0, hap/dip-loid: 2.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [1. 1. 0. 1. 1. 1. 0. 0. 1. 1. 1. 1. 1. 1. 2. 0. 1. 0. 0. 1.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.02027206 0.        ]\cf0 \
\cf2  [0.02273191 0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.0277615  0.        ]\cf0 \
\cf2  [0.03670285 0.        ]\cf0 \
\cf2  [0.03698767 0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.03465843 0.        ]\cf0 \
\cf2  [0.03148729 0.        ]\cf0 \
\cf2  [0.0300761  0.        ]\cf0 \
\cf2  [0.02666408 0.        ]\cf0 \
\cf2  [0.03738501 0.        ]\cf0 \
\cf2  [0.02851382 0.        ]\cf0 \
\cf2  [0.03404527 0.02483235]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.02598348 0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.02760007 0.        ]]\cf0 \
\
\cf2 Runtime: (10492+16610+15609+21068+12334+10548+10299+22405+12376)/9=14,637.889 \cf0 \
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 10492.581629514694 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 16610.1060423851 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 15609.160047769547 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 21068.895077943802 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 12334.016701936722 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 10548.471158742905 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 10299.006145954132 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 22405.133189439774 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.07, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\
\cf2 Inference completed in 12376.598053693771 seconds\cf0 \
\cf2 _________________________\cf0 \
\cf2 Config 11: (Avg. runtime: 14,733.4)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.71467635],[0.28532365]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03951817 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.79219338],[0.20780662]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05001497 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.7645218],[0.2354782]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04273241 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.95756257],[0.04243743],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.62982503],[0.37017497]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04482789 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.76037111],[0.23962889]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05020078 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.55368812],[0.44631188]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0423601 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.45395137],[0.54604863]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03730392 0.02904273]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.69283905],[0.30716095]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04635541 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.97332675],[0.02667325],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.72554859],[0.27445141]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04498746 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([1.],0,0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.74094252],[0.25905748]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03921447 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.69232878],[0.30767122]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04019162 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.84340012],[0.09405966],[0.06254022]). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.63172293],[0.36827707]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0483173 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.76737171],[0.23262829]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04171051 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.792282],[0.207718]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0447969 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.48498522],[0.51501478]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03904166 0.0447689 ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.68287055],[0.31712945]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04616656 0.        ]\cf0 \
\cf2 True s1: 0.1, True s2: 0.0, True nns: 1, True rec rate: 0.0, hap/dip-loid: 2.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [1. 1. 1. 0. 1. 1. 1. 2. 1. 0. 1. 0. 1. 1. 0. 1. 1. 1. 2. 1.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.03951817 0.        ]\cf0 \
\cf2  [0.05001497 0.        ]\cf0 \
\cf2  [0.04273241 0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.04482789 0.        ]\cf0 \
\cf2  [0.05020078 0.        ]\cf0 \
\cf2  [0.0423601  0.        ]\cf0 \
\cf2  [0.03730392 0.02904273]\cf0 \
\cf2  [0.04635541 0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.04498746 0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.03921447 0.        ]\cf0 \
\cf2  [0.04019162 0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.0483173  0.        ]\cf0 \
\cf2  [0.04171051 0.        ]\cf0 \
\cf2  [0.0447969  0.        ]\cf0 \
\cf2  [0.03904166 0.0447689 ]\cf0 \
\cf2  [0.04616656 0.        ]]\cf0 \
\
\cf2 Runtime: 
\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 (
\f0\fs22 \AppleTypeServices 13180
\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 +
\f0\fs22 \AppleTypeServices 14991+22686+13872+10627+14199+17788+12379+13116+14496)/10=14,733.4 
\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 \

\f0\fs22 \AppleTypeServices Inference now running for 0-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 13180.020472764969 seconds\cf0 \
\cf2 Inference now running for 1-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 14991.961083650589 seconds\cf0 \
\cf2 Inference now running for 2-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 22686.251328468323 seconds\cf0 \
\cf2 Inference now running for 3-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 13872.325830698013 seconds\cf0 \
\cf2 Inference now running for 4-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 10627.433670282364 seconds\cf0 \
\cf2 Inference now running for 5-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 14199.310273885727 seconds\cf0 \
\cf2 Inference now running for 6-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 17788.920099258423 seconds\cf0 \
\cf2 Inference now running for 7-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 12379.990395784378 seconds\cf0 \
\cf2 Inference now running for 8-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 13116.154500484467 seconds\cf0 \
\cf2 Inference now running for 9-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 14496.09366941452 seconds\cf0 \
\cf2 Inference now running for 10-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 8465.503386259079 seconds\cf0 \
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\
\cf2 Inference completed in 7551.14213848114 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 8547.429998874664 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 14385.577991724014 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 12276.645823478699 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 9276.266916513443 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 18055.58874440193 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 14709.878876447678 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 18811.640089273453 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.1, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\
\cf2 Inference completed in 12675.561303138733 seconds\cf0 \
\cf2 _________________________\cf0 \
\cf2 Config 12: (Avg. runtime: 14,701.7)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.712689],[0.287311]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05885113 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.68363811],[0.31636189]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06461106 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.44715818],[0.55284182]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04842222 0.0464075 ]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.74285377],[0.25714623]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05929609 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.76486793],[0.23513207]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06866537 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.74358289],[0.25641711]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06195272 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.72947137],[0.27052863],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.389383],[0.610617]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.18382833 0.06835123]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.69843471],[0.30156529],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.66950364],[0.33049636]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06152316 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.61308003],[0.38691997]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06513445 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.83541621],[0.16458379],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.17672572],[0.73641169],[0.08686259]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04116895 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.14408678],[0.73780911],[0.1181041]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02648918 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.74555961],[0.25444039]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05567776 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.78827455],[0.21172545]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01541747 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.58397392],[0.41602608]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06175979 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.83163313],[0.16836687]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06221602 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.53711714],[0.46288286]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07113455 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.21093296],[0.70335259],[0.08571445]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01663638 0.        ]\cf0 \
\cf2 True s1: 0.14, True s2: 0.0, True nns: 1, True rec rate: 0.0, hap/dip-loid: 2.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [1. 1. 2. 1. 1. 1. 0. 2. 0. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.05885113 0.        ]\cf0 \
\cf2  [0.06461106 0.        ]\cf0 \
\cf2  [0.04842222 0.0464075 ]\cf0 \
\cf2  [0.05929609 0.        ]\cf0 \
\cf2  [0.06866537 0.        ]\cf0 \
\cf2  [0.06195272 0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.18382833 0.06835123]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.06152316 0.        ]\cf0 \
\cf2  [0.06513445 0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.04116895 0.        ]\cf0 \
\cf2  [0.02648918 0.        ]\cf0 \
\cf2  [0.05567776 0.        ]\cf0 \
\cf2  [0.01541747 0.        ]\cf0 \
\cf2  [0.06175979 0.        ]\cf0 \
\cf2  [0.06221602 0.        ]\cf0 \
\cf2  [0.07113455 0.        ]\cf0 \
\cf2  [0.01663638 0.        ]]\cf0 \
\
\cf2 Runtime: (11912+10686+16671+10654+18528+13456+15522+27113+13227+9248)/10=14,701.7 \cf0 \
\cf2 Inference now running for 10-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 11912.57184100151 seconds\cf0 \
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 10686.745275735855 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 16671.233625650406 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 10654.818906545639 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 18528.62091398239 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 13456.809612751007 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 15522.786016702652 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 27113.43596982956 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 13227.052818059921 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.14, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\
\cf2 Inference completed in 9248.189970970154 seconds\cf0 \
\pard\pardeftab720\partightenfactor0

\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 \cf2 \
\pard\pardeftab720\partightenfactor0

\f0\fs22 \AppleTypeServices \cf2 _________________________\cf0 \
\cf2 Config 13: (Avg. runtime: 14,682.727)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.55080663],[0.44919337]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08438184 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.66433615],[0.33566385]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06632285 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.65972313],[0.34027687]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08539799 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.67324178],[0.32675822]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10075591 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.73672917],[0.26327083]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06919478 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.73601349],[0.26398651]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07633098 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.49469568],[0.48071047],[0.02459385]). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.68604841],[0.31395159]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06233991 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.59889838],[0.40110162]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06727842 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.68648814],[0.31351186]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05399422 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.41741174],[0.58258826]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08230204 0.07546136]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.55037498],[0.44962502]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07694492 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.51647935],[0.48352065]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0845968 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.6662847],[0.3337153]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07131667 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.40539255],[0.59460745]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05146234 0.05571678]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.63123298],[0.36876702]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07354683 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.62085672],[0.37914328]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08195139 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.53120915],[0.46879085]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08902541 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.6539365],[0.3460635]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08348679 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.59541366],[0.40458634]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0731627 0.       ]\cf0 \
\cf2 True s1: 0.18, True s2: 0.0, True nns: 1, True rec rate: 0.0, hap/dip-loid: 2.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 2. 1. 1. 1. 2. 1. 1. 1. 1. 1.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.08438184 0.        ]\cf0 \
\cf2  [0.06632285 0.        ]\cf0 \
\cf2  [0.08539799 0.        ]\cf0 \
\cf2  [0.10075591 0.        ]\cf0 \
\cf2  [0.06919478 0.        ]\cf0 \
\cf2  [0.07633098 0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.06233991 0.        ]\cf0 \
\cf2  [0.06727842 0.        ]\cf0 \
\cf2  [0.05399422 0.        ]\cf0 \
\cf2  [0.08230204 0.07546136]\cf0 \
\cf2  [0.07694492 0.        ]\cf0 \
\cf2  [0.0845968  0.        ]\cf0 \
\cf2  [0.07131667 0.        ]\cf0 \
\cf2  [0.05146234 0.05571678]\cf0 \
\cf2  [0.07354683 0.        ]\cf0 \
\cf2  [0.08195139 0.        ]\cf0 \
\cf2  [0.08902541 0.        ]\cf0 \
\cf2  [0.08348679 0.        ]\cf0 \
\cf2  [0.0731627  0.        ]]\cf0 \
\
\cf2 Runtime: (8061+12223+14194+14460+22269+16178+16405+16725+14413+15354+11228)/11=14,682.727 \cf0 \
\cf2 Inference now running for 9-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 8061.577085971832 seconds\cf0 \
\cf2 Inference now running for 10-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 12223.120854139328 seconds\cf0 \
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 14194.001447200775 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 14460.763982772827 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 22269.49497127533 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 16178.760963439941 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 16405.78565430641 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 16725.046019792557 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 14413.645455121994 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 15354.403183698654 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.18, 0.0, 1, 0.0, 2.0, 20.0]\cf0 \
\
\cf2 Inference completed in 11228.622014284134 seconds\cf0 \
\cf2 _________________________\cf0 \
\cf2 Config 14: (Avg. runtime: 15,171.222)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.73708432],[0.26291568]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01973385 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.72302751],[0.24192742],[0.03504507]). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.41631526],[0.47713861],[0.10654613]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.00971041 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.79920625],[0.20079375]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05139984 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.79372554],[0.20627446]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01835588 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.04714852],[0.68698202],[0.26586946]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01450476 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.5048557],[0.36394078],[0.13120352]). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.675685],[0.324315]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0307933 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.73138201],[0.26861799]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05410661 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.59211113],[0.40788887]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03065296 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.85967332],[0.14032668]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02241261 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.72690499],[0.21572099],[0.05737402]). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.75216745],[0.24783255]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02644257 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.6480329],[0.3519671]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01412572 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.98937542],[0.01062458],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.65924034],[0.34075966]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02049547 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.77730202],[0.22269798]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03174819 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.61354723],[0.38645277]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0388447 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.12754141],[0.71239623],[0.16006237]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01708058 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.62856255],[0.37143745]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03807264 0.        ]\cf0 \
\cf2 True s1: 0.05, True s2: 0.05, True nns: 2, True rec rate: 2.5, hap/dip-loid: 2.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [1. 0. 1. 1. 1. 1. 0. 1. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 1. 1.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.01973385 0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.00971041 0.        ]\cf0 \
\cf2  [0.05139984 0.        ]\cf0 \
\cf2  [0.01835588 0.        ]\cf0 \
\cf2  [0.01450476 0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.0307933  0.        ]\cf0 \
\cf2  [0.05410661 0.        ]\cf0 \
\cf2  [0.03065296 0.        ]\cf0 \
\cf2  [0.02241261 0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.02644257 0.        ]\cf0 \
\cf2  [0.01412572 0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.02049547 0.        ]\cf0 \
\cf2  [0.03174819 0.        ]\cf0 \
\cf2  [0.0388447  0.        ]\cf0 \
\cf2  [0.01708058 0.        ]\cf0 \
\cf2  [0.03807264 0.        ]]\cf0 \
\
\cf2 Runtime: (11506+10973+22350+15037+19003+17894+14837+14821+10120)/9=15,171.222 \cf0 \
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 11506.138905763626 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 10973.126907110214 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 22350.585023880005 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 15037.162858963013 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 19003.233868837357 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 17894.82485461235 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 14837.69267487526 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 14821.332968711853 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 2.0, 20.0]\cf0 \
\
\cf2 Inference completed in 10120.25890135765 seconds\cf0 \
\cf2 _________________________\cf0 \
\cf2 Config 15: (Avg. runtime: 13,390.4)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.67808555],[0.32191445]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04453863 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.62354133],[0.37645867]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0252345 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.72130028],[0.27869972]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07666806 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.83055593],[0.16944407]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04514691 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.77654996],[0.22345004]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04637912 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.82351027],[0.17648973]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04044497 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.54449269],[0.45550731]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05529395 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.77512693],[0.22487307]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04044144 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.56000224],[0.43999776]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.029748 0.      ]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.28017754],[0.53955523],[0.18026723]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01148534 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.54552586],[0.45447414]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05058077 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.72899919],[0.27100081]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09131003 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.57768277],[0.42231723]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04857893 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.64535514],[0.35464486]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02217667 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.60625213],[0.39374787]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07726148 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.7794953],[0.2205047]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02220103 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.62115152],[0.37884848]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02834053 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.55661348],[0.44338652]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02169303 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.39812179],[0.60187821]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03558241 0.06582896]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.6683584],[0.3316416]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07606311 0.        ]\cf0 \
\cf2 True s1: 0.07, True s2: 0.09, True nns: 2, True rec rate: 2.5, hap/dip-loid: 2.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 2. 1.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.04453863 0.        ]\cf0 \
\cf2  [0.0252345  0.        ]\cf0 \
\cf2  [0.07666806 0.        ]\cf0 \
\cf2  [0.04514691 0.        ]\cf0 \
\cf2  [0.04637912 0.        ]\cf0 \
\cf2  [0.04044497 0.        ]\cf0 \
\cf2  [0.05529395 0.        ]\cf0 \
\cf2  [0.04044144 0.        ]\cf0 \
\cf2  [0.029748   0.        ]\cf0 \
\cf2  [0.01148534 0.        ]\cf0 \
\cf2  [0.05058077 0.        ]\cf0 \
\cf2  [0.09131003 0.        ]\cf0 \
\cf2  [0.04857893 0.        ]\cf0 \
\cf2  [0.02217667 0.        ]\cf0 \
\cf2  [0.07726148 0.        ]\cf0 \
\cf2  [0.02220103 0.        ]\cf0 \
\cf2  [0.02834053 0.        ]\cf0 \
\cf2  [0.02169303 0.        ]\cf0 \
\cf2  [0.03558241 0.06582896]\cf0 \
\cf2  [0.07606311 0.        ]]\cf0 \
\
\cf2 Runtime: (10324+18682+12790+10257+13843+8403+10500+10855+12902+25348)/10=13,390.4 \cf0 \
\cf2 Inference now running for 10-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 10324.844376325607 seconds\cf0 \
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 18682.103980779648 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 12790.55844950676 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 10257.96287894249 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 13843.61685848236 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 8403.017855644226 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 10500.06520485878 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 10855.005936145782 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 12902.242804050446 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 2.0, 20.0]\cf0 \
\
\cf2 Inference completed in 25348.775900363922 seconds\cf0 \
\cf2 _________________________\cf0 \
\cf2 Config 16: (Avg. runtime: 15,517)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.33657761],[0.5395591],[0.12386329]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01316499 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.66231989],[0.33768011]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04195449 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.74099073],[0.25900927]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06080155 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.54960311],[0.45039689]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06201295 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.73934792],[0.26065208]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05469394 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.64683443],[0.35316557]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05349837 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.72882732],[0.27117268]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05519433 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.62182323],[0.37817677]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.11019116 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.95510217],[0.04489783],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.62844121],[0.37155879]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08475492 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.63274149],[0.36725851]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02761787 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.39957956],[0.60042044]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0351339  0.04200544]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.89975472],[0.10024528]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06541238 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.5729753],[0.4270247]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0972506 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.45075176],[0.54924824]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0361301  0.03484116]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.36892742],[0.63107258]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02440719 0.04153562]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.78228049],[0.21771951]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04749345 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.59345012],[0.40654988]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09889812 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.73695879],[0.26304121]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.058922 0.      ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.54467604],[0.45532396]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10256059 0.        ]\cf0 \
\cf2 True s1: 0.09, True s2: 0.12, True nns: 2, True rec rate: 2.5, hap/dip-loid: 2.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 2. 1. 1. 2. 2. 1. 1. 1. 1.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.01316499 0.        ]\cf0 \
\cf2  [0.04195449 0.        ]\cf0 \
\cf2  [0.06080155 0.        ]\cf0 \
\cf2  [0.06201295 0.        ]\cf0 \
\cf2  [0.05469394 0.        ]\cf0 \
\cf2  [0.05349837 0.        ]\cf0 \
\cf2  [0.05519433 0.        ]\cf0 \
\cf2  [0.11019116 0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.08475492 0.        ]\cf0 \
\cf2  [0.02761787 0.        ]\cf0 \
\cf2  [0.0351339  0.04200544]\cf0 \
\cf2  [0.06541238 0.        ]\cf0 \
\cf2  [0.0972506  0.        ]\cf0 \
\cf2  [0.0361301  0.03484116]\cf0 \
\cf2  [0.02440719 0.04153562]\cf0 \
\cf2  [0.04749345 0.        ]\cf0 \
\cf2  [0.09889812 0.        ]\cf0 \
\cf2  [0.058922   0.        ]\cf0 \
\cf2  [0.10256059 0.        ]]\cf0 \
\
\cf2 Runtime: (20540+14250+11753+21435+15364+13420+14729+12645)/8=15,517 \cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 20540.269781589508 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 14250.821898937225 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 11753.122374773026 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 21435.865760087967 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 15364.298820495605 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 13420.604891300201 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 14729.380863666534 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 2.0, 20.0]\cf0 \
\
\cf2 Inference completed in 12645.890846252441 seconds\cf0 \
\pard\pardeftab720\partightenfactor0

\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 \cf2 \
\pard\pardeftab720\partightenfactor0

\f0\fs22 \AppleTypeServices \cf2 _________________________\cf0 \
\cf2 Config 17: (Avg. runtime: 17,107.5)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.42894166],[0.57105834]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04282791 0.03583427]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.50204614],[0.49795386]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.14687605 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.25256982],[0.74743018]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0625952  0.05298658]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.49441224],[0.50558776]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03805606 0.15063917]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.44683824],[0.55316176]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08371804 0.07514295]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.42002594],[0.57997406]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.16114011 0.0552275 ]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.33106432],[0.66893568]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05915067 0.0649132 ]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.45843403],[0.54156597]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02620325 0.08994337]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.65601659],[0.34398341]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10341051 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.56619365],[0.43380635]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.15426465 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.53591828],[0.46408172]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.13912131 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.29733731],[0.70266269]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06314662 0.05358939]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.67061531],[0.32938469]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07027985 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.4132881],[0.5867119]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0576693  0.06509782]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.44505815],[0.55494185]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06637037 0.07193479]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.21440459],[0.78559541]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06752592 0.08395504]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.39179579],[0.60820421]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06068712 0.06956422]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.65353076],[0.32303199],[0.02343726]). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.62460348],[0.37539652]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0879504 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.49060008],[0.50939992]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09079694 0.02708202]\cf0 \
\cf2 True s1: 0.14, True s2: 0.18, True nns: 2, True rec rate: 2.5, hap/dip-loid: 2.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [2. 1. 2. 2. 2. 2. 2. 2. 1. 1. 1. 2. 1. 2. 2. 2. 2. 0. 1. 2.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.04282791 0.03583427]\cf0 \
\cf2  [0.14687605 0.        ]\cf0 \
\cf2  [0.0625952  0.05298658]\cf0 \
\cf2  [0.03805606 0.15063917]\cf0 \
\cf2  [0.08371804 0.07514295]\cf0 \
\cf2  [0.16114011 0.0552275 ]\cf0 \
\cf2  [0.05915067 0.0649132 ]\cf0 \
\cf2  [0.02620325 0.08994337]\cf0 \
\cf2  [0.10341051 0.        ]\cf0 \
\cf2  [0.15426465 0.        ]\cf0 \
\cf2  [0.13912131 0.        ]\cf0 \
\cf2  [0.06314662 0.05358939]\cf0 \
\cf2  [0.07027985 0.        ]\cf0 \
\cf2  [0.0576693  0.06509782]\cf0 \
\cf2  [0.06637037 0.07193479]\cf0 \
\cf2  [0.06752592 0.08395504]\cf0 \
\cf2  [0.06068712 0.06956422]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.0879504  0.        ]\cf0 \
\cf2  [0.09079694 0.02708202]]\cf0 \
\
\cf2 Runtime: (10045+16097+22313+26007+15173+8193+18467+20565)/8=17,107.5 \cf0 \
\
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 10045.369660139084 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 16097.560119390488 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 22313.009914636612 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 26007.52103495598 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 15173.373172044754 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 8193.891189575195 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 18467.575165510178 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 20565.514119148254 seconds\cf0 \
\pard\pardeftab720\partightenfactor0

\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 \cf2 \
\pard\pardeftab720\partightenfactor0

\f0\fs22 \AppleTypeServices \cf2 _________________________\cf0 \
\cf2 Config 18: (Avg. runtime: 14,885.5)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.44000601],[0.55999399]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04216442 0.05387071]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.55377275],[0.44622725]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10621819 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.53912681],[0.46087319]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.14446565 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.56052445],[0.43947555]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.12858716 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.67453264],[0.32546736]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02734499 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.31095016],[0.68904984]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06591427 0.0397504 ]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.31638675],[0.68361325]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06892461 0.05878975]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.46361983],[0.53638017]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01468117 0.05296577]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.43798002],[0.56201998]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05033182 0.05897938]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.52763701],[0.47236299]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05377531 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.59628479],[0.40371521]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08721102 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.54924694],[0.45075306]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0506021 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.62897696],[0.37102304]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06467593 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.35935299],[0.64064701]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06653679 0.04879041]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.35804161],[0.64195839]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04808924 0.06426122]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.47233822],[0.52766178]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08945222 0.03887868]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.52753232],[0.47246768]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07936301 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.60107684],[0.39892316]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.13507891 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.33217394],[0.66782606]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04369252 0.05966516]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.50488402],[0.49511598]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04963808 0.        ]\cf0 \
\cf2 True s1: 0.14, True s2: 0.18, True nns: 2, True rec rate: 0.0, hap/dip-loid: 2.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [2. 1. 1. 1. 1. 2. 2. 2. 2. 1. 1. 1. 1. 2. 2. 2. 1. 1. 2. 1.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.04216442 0.05387071]\cf0 \
\cf2  [0.10621819 0.        ]\cf0 \
\cf2  [0.14446565 0.        ]\cf0 \
\cf2  [0.12858716 0.        ]\cf0 \
\cf2  [0.02734499 0.        ]\cf0 \
\cf2  [0.06591427 0.0397504 ]\cf0 \
\cf2  [0.06892461 0.05878975]\cf0 \
\cf2  [0.01468117 0.05296577]\cf0 \
\cf2  [0.05033182 0.05897938]\cf0 \
\cf2  [0.05377531 0.        ]\cf0 \
\cf2  [0.08721102 0.        ]\cf0 \
\cf2  [0.0506021  0.        ]\cf0 \
\cf2  [0.06467593 0.        ]\cf0 \
\cf2  [0.06653679 0.04879041]\cf0 \
\cf2  [0.04808924 0.06426122]\cf0 \
\cf2  [0.08945222 0.03887868]\cf0 \
\cf2  [0.07936301 0.        ]\cf0 \
\cf2  [0.13507891 0.        ]\cf0 \
\cf2  [0.04369252 0.05966516]\cf0 \
\cf2  [0.04963808 0.        ]]\cf0 \
\
\cf2 Runtime: (13415+11157+13115+11922+20500+14224+19465+15379+11653+18025)/10=14,885.5 \cf0 \
\cf2 Inference now running for 10-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 13415.02264213562 seconds\cf0 \
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 11157.017099380493 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 13115.773391246796 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 11922.243913650513 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 20500.193168640137 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 14224.452616214752 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 19465.125016212463 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 15379.763825893402 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 11653.169325113297 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 0.0, 2.0, 20.0]\cf0 \
\
\cf2 Inference completed in 18025.373970985413 seconds\cf0 \
\cf2 _________________________\cf0 \
\cf2 Config 19: (Avg. runtime: 12,504.833)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.62652912],[0.37347088]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08204897 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.47915467],[0.52084533]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.14193878 0.01461874]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.41600122],[0.58399878]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08408862 0.07798347]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.52662537],[0.47337463]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10974072 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.3600243],[0.6399757]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06831338 0.0668723 ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.54814605],[0.45185395]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.13338264 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.42349583],[0.57650417]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04853916 0.06357548]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.28519534],[0.71480466]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06649431 0.05440835]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.57491439],[0.42508561]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07746506 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.59025326],[0.40974674]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.13443639 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.29779754],[0.70220246]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03819707 0.06570477]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.21513775],[0.78486225]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.15627679 0.09102867]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.59776559],[0.40223441]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.11198337 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.3714015],[0.6285985]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04488091 0.0498927 ]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.54972352],[0.45027648]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.15664081 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.37175367],[0.62824633]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0647525  0.04648899]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.38643487],[0.61356513]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08140918 0.092314  ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.31141831],[0.68858169]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04773473 0.12739075]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.58808607],[0.41191393]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08545134 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.41614108],[0.58385892]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04881891 0.11799759]\cf0 \
\cf2 True s1: 0.14, True s2: 0.18, True nns: 2, True rec rate: 15.0, hap/dip-loid: 2.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [1. 2. 2. 1. 2. 1. 2. 2. 1. 1. 2. 2. 1. 2. 1. 2. 2. 2. 1. 2.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.08204897 0.        ]\cf0 \
\cf2  [0.14193878 0.01461874]\cf0 \
\cf2  [0.08408862 0.07798347]\cf0 \
\cf2  [0.10974072 0.        ]\cf0 \
\cf2  [0.06831338 0.0668723 ]\cf0 \
\cf2  [0.13338264 0.        ]\cf0 \
\cf2  [0.04853916 0.06357548]\cf0 \
\cf2  [0.06649431 0.05440835]\cf0 \
\cf2  [0.07746506 0.        ]\cf0 \
\cf2  [0.13443639 0.        ]\cf0 \
\cf2  [0.03819707 0.06570477]\cf0 \
\cf2  [0.15627679 0.09102867]\cf0 \
\cf2  [0.11198337 0.        ]\cf0 \
\cf2  [0.04488091 0.0498927 ]\cf0 \
\cf2  [0.15664081 0.        ]\cf0 \
\cf2  [0.0647525  0.04648899]\cf0 \
\cf2  [0.08140918 0.092314  ]\cf0 \
\cf2  [0.04773473 0.12739075]\cf0 \
\cf2  [0.08545134 0.        ]\cf0 \
\cf2  [0.04881891 0.11799759]]\cf0 \
\
\cf2 Runtime: (8129+8774+17606+13818+12547+14155)/6=12,504.833 \cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 8129.842204332352 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 8774.28646683693 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 17606.631549596786 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 13818.060913324356 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 12547.826016664505 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 15.0, 2.0, 20.0]\cf0 \
\
\cf2 Inference completed in 14155.383000612259 seconds\cf0 \
\cf2 _________________________\cf0 \
\cf2 Config 20: (Avg. runtime: 14,650.333)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.3443056],[0.6556944]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05688755 0.05781179]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.14217184],[0.85782816]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03974325 0.08019636]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.42474476],[0.57525524]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07454202 0.07903855]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.528943],[0.471057]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04738494 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.38225575],[0.61774425]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08327637 0.02324039]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.98293575],[0.01706425],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.36624098],[0.63375902]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.12096453 0.07014934]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.62683634],[0.37316366]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.12695109 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.62242704],[0.37757296]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08622858 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.22099919],[0.77900081]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04513534 0.07024209]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.30403989],[0.69596011]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06428924 0.0612112 ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.47461677],[0.52538323]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.15122184 0.04321287]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.50120627],[0.49879373]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.12317316 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.34203357],[0.65796643]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05215155 0.0688269 ]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.55445899],[0.44554101]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.15093604 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.28910917],[0.56519471],[0.14569612]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01930441 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.58687312],[0.41312688]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09684686 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.48038165],[0.51961835]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03159935 0.11669738]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.60313062],[0.39686938]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09477783 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.58068613],[0.41931387]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.1300213 0.       ]\cf0 \
\cf2 True s1: 0.14, True s2: 0.18, True nns: 2, True rec rate: 30.0, hap/dip-loid: 2.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [2. 2. 2. 1. 2. 0. 2. 1. 1. 2. 2. 2. 1. 2. 1. 1. 1. 2. 1. 1.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.05688755 0.05781179]\cf0 \
\cf2  [0.03974325 0.08019636]\cf0 \
\cf2  [0.07454202 0.07903855]\cf0 \
\cf2  [0.04738494 0.        ]\cf0 \
\cf2  [0.08327637 0.02324039]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.12096453 0.07014934]\cf0 \
\cf2  [0.12695109 0.        ]\cf0 \
\cf2  [0.08622858 0.        ]\cf0 \
\cf2  [0.04513534 0.07024209]\cf0 \
\cf2  [0.06428924 0.0612112 ]\cf0 \
\cf2  [0.15122184 0.04321287]\cf0 \
\cf2  [0.12317316 0.        ]\cf0 \
\cf2  [0.05215155 0.0688269 ]\cf0 \
\cf2  [0.15093604 0.        ]\cf0 \
\cf2  [0.01930441 0.        ]\cf0 \
\cf2  [0.09684686 0.        ]\cf0 \
\cf2  [0.03159935 0.11669738]\cf0 \
\cf2  [0.09477783 0.        ]\cf0 \
\cf2  [0.1300213  0.        ]]\cf0 \
\
\cf2 Runtime: (8388+19868+8877+25720+8900+10821+18124+20711+10444)/9=14,650.333 \cf0 \
\
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 8388.766564130783 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 19868.117985725403 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 8877.00636935234 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 25720.761945724487 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 8900.144987821579 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 10821.240859270096 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 18124.082875967026 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 20711.28785610199 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 30.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 10444.415792703629 seconds\cf0 \
\pard\pardeftab720\partightenfactor0

\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 \cf2 \
\pard\pardeftab720\partightenfactor0

\f0\fs22 \AppleTypeServices \cf2 _________________________\cf0 \
\cf2 Config 21: (Avg. runtime: 14,373.444)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.46023968],[0.53976032]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02978786 0.06850839]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.36345029],[0.63654971]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05446884 0.05894856]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.62778397],[0.37221603]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.14076543 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.53261707],[0.46738293]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06560249 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.35250563],[0.64749437]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05355266 0.0724321 ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.62400585],[0.37599415]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0893802 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.64173785],[0.35826215]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07451817 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.48935362],[0.51064638]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.13463549 0.02604653]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.40354647],[0.59645353]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05468904 0.0586205 ]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.7188036],[0.2811964]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02235596 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.57988135],[0.42011865]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.13611868 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.57018513],[0.42981487]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09350921 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.21378712],[0.78621288]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06613313 0.07762273]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.61135386],[0.38864614]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06705193 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.52131361],[0.47868639]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.12767939 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.65893834],[0.34106166]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06317392 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.56727399],[0.43272601]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.15092807 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.56658595],[0.43341405]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.13817587 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.50073377],[0.49926623]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10381387 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.43917105],[0.56082895]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06337397 0.06624384]\cf0 \
\cf2 True s1: 0.14, True s2: 0.18, True nns: 2, True rec rate: 49.0, hap/dip-loid: 2.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [2. 2. 1. 1. 2. 1. 1. 2. 2. 1. 1. 1. 2. 1. 1. 1. 1. 1. 1. 2.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.02978786 0.06850839]\cf0 \
\cf2  [0.05446884 0.05894856]\cf0 \
\cf2  [0.14076543 0.        ]\cf0 \
\cf2  [0.06560249 0.        ]\cf0 \
\cf2  [0.05355266 0.0724321 ]\cf0 \
\cf2  [0.0893802  0.        ]\cf0 \
\cf2  [0.07451817 0.        ]\cf0 \
\cf2  [0.13463549 0.02604653]\cf0 \
\cf2  [0.05468904 0.0586205 ]\cf0 \
\cf2  [0.02235596 0.        ]\cf0 \
\cf2  [0.13611868 0.        ]\cf0 \
\cf2  [0.09350921 0.        ]\cf0 \
\cf2  [0.06613313 0.07762273]\cf0 \
\cf2  [0.06705193 0.        ]\cf0 \
\cf2  [0.12767939 0.        ]\cf0 \
\cf2  [0.06317392 0.        ]\cf0 \
\cf2  [0.15092807 0.        ]\cf0 \
\cf2  [0.13817587 0.        ]\cf0 \
\cf2  [0.10381387 0.        ]\cf0 \
\cf2  [0.06337397 0.06624384]]\cf0 \
\
\cf2 Runtime: (12712+14344+16572+13339+13223+13252+13256+24249+8414)/9=14,373.444 \cf0 \
\
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 12712.440797805786 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 14344.480811834335 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 16572.542068004608 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 13339.154584407806 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 13223.114846229553 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 13252.078728437424 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 13256.924776792526 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 24249.933240652084 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 49.0, 2.0, 20.0]\cf0 \
\cf2 Inference completed in 8414.311468601227 seconds\cf0 \
\pard\pardeftab720\partightenfactor0

\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 \cf2 \
\pard\pardeftab720\partightenfactor0

\f0\fs22 \AppleTypeServices \cf2 _________________________\cf0 \
\cf2 Config 22: (Avg. runtime: 18,192.25)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.50437447],[0.49562553]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03339806 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.45230505],[0.54769495]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02626923 0.04067552]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.22806337],[0.77193663]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.11674109 0.03676414]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.52773334],[0.47226666]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04765634 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.64190659],[0.35809341]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07701666 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.46538908],[0.53461092]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06202816 0.03843393]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.50393761],[0.49606239]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05269725 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.66454701],[0.33545299]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03934614 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.43397772],[0.56602228]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0242018  0.09916913]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.47065319],[0.52934681]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06039032 0.02900566]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.63290137],[0.36709863]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04062354 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.61666998],[0.38333002]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03397848 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.64574071],[0.35425929]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04285886 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.32204042],[0.67795958]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.057219   0.05831925]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.37691033],[0.62308967]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03050699 0.06254821]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.59488533],[0.40511467]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05888447 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.57650855],[0.42349145]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08524464 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.58505973],[0.41494027]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03090698 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.66334477],[0.33665523]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10100381 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.55764928],[0.44235072]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.12352449 0.        ]\cf0 \
\cf2 True s1: 0.05, True s2: 0.02, True nns: 2, True rec rate: 0.0, hap/dip-loid: 4.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [1. 2. 2. 1. 1. 2. 1. 1. 2. 2. 1. 1. 1. 2. 2. 1. 1. 1. 1. 1.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.03339806 0.        ]\cf0 \
\cf2  [0.02626923 0.04067552]\cf0 \
\cf2  [0.11674109 0.03676414]\cf0 \
\cf2  [0.04765634 0.        ]\cf0 \
\cf2  [0.07701666 0.        ]\cf0 \
\cf2  [0.06202816 0.03843393]\cf0 \
\cf2  [0.05269725 0.        ]\cf0 \
\cf2  [0.03934614 0.        ]\cf0 \
\cf2  [0.0242018  0.09916913]\cf0 \
\cf2  [0.06039032 0.02900566]\cf0 \
\cf2  [0.04062354 0.        ]\cf0 \
\cf2  [0.03397848 0.        ]\cf0 \
\cf2  [0.04285886 0.        ]\cf0 \
\cf2  [0.057219   0.05831925]\cf0 \
\cf2  [0.03050699 0.06254821]\cf0 \
\cf2  [0.05888447 0.        ]\cf0 \
\cf2  [0.08524464 0.        ]\cf0 \
\cf2  [0.03090698 0.        ]\cf0 \
\cf2  [0.10100381 0.        ]\cf0 \
\cf2  [0.12352449 0.        ]]\cf0 \
\
\cf2 Runtime: (12224+19671+18007+22238+23974+14370+18530+16524)/8=18,192.25 \cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 12224.531028985977 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 19671.00023150444 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 18007.30274438858 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 22238.494837522507 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 23974.687962770462 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 14370.862899541855 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 18530.311004161835 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.05, 0.02, 2, 0.0, 4.0, 20.0]\cf0 \
\
\cf2 Inference completed in 16524.743993520737 seconds\cf0 \
\cf2 _________________________\cf0 \
\cf2 Config 23: (Avg. runtime: 15,361.4)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.46147774],[0.53852226]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.12305734 0.02371623]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.41675145],[0.58324855]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05822548 0.06121004]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.2289321],[0.7710679]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05993329 0.05830968]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.47329786],[0.52670214]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06567602 0.15635564]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.23002033],[0.76997967]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05694832 0.09105266]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.27719407],[0.72280593]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04605732 0.05603168]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.6233884],[0.3766116]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06205065 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.41704457],[0.58295543]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03679088 0.13665185]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.58113389],[0.41886611]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07630181 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.40749294],[0.59250706]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02842126 0.06041113]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.3399355],[0.6600645]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08482884 0.0868758 ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.69040564],[0.30959436]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.12424512 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.34739496],[0.65260504]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07318188 0.07869772]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.47729658],[0.52270342]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.1429103  0.04769111]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.55159073],[0.44840927]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09065398 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.4823287],[0.5176713]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07460856 0.00869446]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.52764684],[0.47235316]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06595875 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.43905059],[0.56094941]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08293837 0.08795538]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.5343307],[0.4656693]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07156627 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.50346004],[0.49653996]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08416523 0.        ]\cf0 \
\cf2 True s1: 0.05, True s2: 0.07, True nns: 2, True rec rate: 0.0, hap/dip-loid: 4.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [2. 2. 2. 2. 2. 2. 1. 2. 1. 2. 2. 1. 2. 2. 1. 2. 1. 2. 1. 1.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.12305734 0.02371623]\cf0 \
\cf2  [0.05822548 0.06121004]\cf0 \
\cf2  [0.05993329 0.05830968]\cf0 \
\cf2  [0.06567602 0.15635564]\cf0 \
\cf2  [0.05694832 0.09105266]\cf0 \
\cf2  [0.04605732 0.05603168]\cf0 \
\cf2  [0.06205065 0.        ]\cf0 \
\cf2  [0.03679088 0.13665185]\cf0 \
\cf2  [0.07630181 0.        ]\cf0 \
\cf2  [0.02842126 0.06041113]\cf0 \
\cf2  [0.08482884 0.0868758 ]\cf0 \
\cf2  [0.12424512 0.        ]\cf0 \
\cf2  [0.07318188 0.07869772]\cf0 \
\cf2  [0.1429103  0.04769111]\cf0 \
\cf2  [0.09065398 0.        ]\cf0 \
\cf2  [0.07460856 0.00869446]\cf0 \
\cf2  [0.06595875 0.        ]\cf0 \
\cf2  [0.08293837 0.08795538]\cf0 \
\cf2  [0.07156627 0.        ]\cf0 \
\cf2  [0.08416523 0.        ]]\cf0 \
\
\cf2 Runtime: (10381+19313+17233+13250+11193+22959+15549+20806+7419+15511)/10=15,361.4 \cf0 \
\cf2 Inference now running for 10-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 10381.54436659813 seconds\cf0 \
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 19313.585329294205 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 17233.919795513153 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 13250.252164363861 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 11193.873201608658 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 22959.561131715775 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 15549.320329904556 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 20806.912570476532 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 7419.454280376434 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.05, 0.07, 2, 0.0, 4.0, 20.0]\cf0 \
\
\cf2 Inference completed in 15511.791282653809 seconds\cf0 \
\cf2 _________________________\cf0 \
\cf2 Config 24: (Avg. runtime: 15,827.2 )\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.50333444],[0.49666556]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.17172898 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.23023802],[0.76976198]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.11043104 0.15025091]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.49186105],[0.50813895]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03097431 0.11153818]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.47126601],[0.52873399]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07497119 0.14494365]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.41999439],[0.58000561]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10862244 0.10330414]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.36224329],[0.63775671]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05149015 0.05997114]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.34034642],[0.65965358]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09818006 0.09910933]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.63027023],[0.36972977]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02254507 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.40926943],[0.59073057]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.12430954 0.11509057]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.32453084],[0.67546916]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05031469 0.16441622]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.26850658],[0.73149342]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07069363 0.09243167]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.30557506],[0.69442494]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09327903 0.09890451]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.55109505],[0.44890495]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0973526 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.33037772],[0.66962228]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.077573   0.09316246]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.34472439],[0.65527561]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05288435 0.1720362 ]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.20920526],[0.79079474]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10651976 0.11321482]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.23721766],[0.76278234]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05008309 0.13010892]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.53969506],[0.46030494]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10320267 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.54593371],[0.45406629]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03437133 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.51523082],[0.48476918]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10315864 0.        ]\cf0 \
\cf2 True s1: 0.07, True s2: 0.09, True nns: 2, True rec rate: 0.0, hap/dip-loid: 4.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [1. 2. 2. 2. 2. 2. 2. 1. 2. 2. 2. 2. 1. 2. 2. 2. 2. 1. 1. 1.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.17172898 0.        ]\cf0 \
\cf2  [0.11043104 0.15025091]\cf0 \
\cf2  [0.03097431 0.11153818]\cf0 \
\cf2  [0.07497119 0.14494365]\cf0 \
\cf2  [0.10862244 0.10330414]\cf0 \
\cf2  [0.05149015 0.05997114]\cf0 \
\cf2  [0.09818006 0.09910933]\cf0 \
\cf2  [0.02254507 0.        ]\cf0 \
\cf2  [0.12430954 0.11509057]\cf0 \
\cf2  [0.05031469 0.16441622]\cf0 \
\cf2  [0.07069363 0.09243167]\cf0 \
\cf2  [0.09327903 0.09890451]\cf0 \
\cf2  [0.0973526  0.        ]\cf0 \
\cf2  [0.077573   0.09316246]\cf0 \
\cf2  [0.05288435 0.1720362 ]\cf0 \
\cf2  [0.10651976 0.11321482]\cf0 \
\cf2  [0.05008309 0.13010892]\cf0 \
\cf2  [0.10320267 0.        ]\cf0 \
\cf2  [0.03437133 0.        ]\cf0 \
\cf2  [0.10315864 0.        ]]\cf0 \
\
\cf2 Runtime: (12931+25032+20830+22590+13142+15483+18964+9064+6646+13590)/10=15,827.2 \cf0 \
\cf2 Inference now running for 10-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 12931.06608080864 seconds\cf0 \
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 25032.967893123627 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 20830.040473222733 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 22590.86991429329 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 13142.1440243721 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 15483.408960103989 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 18964.24902009964 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 9064.178822755814 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\cf2 Inference completed in 6646.717956542969 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 0.0, 4.0, 20.0]\cf0 \
\
\cf2 Inference completed in 13590.518376111984 seconds\cf0 \
\cf2 _________________________\cf0 \
\cf2 config 25: (Avg. runtime: 13,352.571)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.70281849],[0.29718151]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02496199 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.73978768],[0.26021232]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02476321 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.81435814],[0.18564186]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02720702 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.73661984],[0.26338016]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04999239 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.70577761],[0.29422239]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01958209 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.62131837],[0.37868163]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01925294 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.59300615],[0.40699385]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03872289 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.0217198],[0.74227484],[0.23600536]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01852869 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.9652925],[0.0347075],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.80903847],[0.19096153]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02054691 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.2390486],[0.65189123],[0.10906017]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01168833 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.98545587],[0.01454413],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.7308538],[0.2691462]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02330663 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.03313392],[0.74737892],[0.21948716]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01594704 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.59978546],[0.40021454]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02994411 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.76070554],[0.23929446]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04717568 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.48469549],[0.51530451]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05394448 0.02537037]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.01685766],[0.67040576],[0.31273658]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.01801949 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.64570654],[0.35429346]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05725785 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.63731596],[0.36268404]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05265245 0.        ]\cf0 \
\cf2 True s1: 0.05, True s2: 0.05, True nns: 2, True rec rate: 2.5, hap/dip-loid: 5.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 1. 2. 1. 1. 1.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.02496199 0.        ]\cf0 \
\cf2  [0.02476321 0.        ]\cf0 \
\cf2  [0.02720702 0.        ]\cf0 \
\cf2  [0.04999239 0.        ]\cf0 \
\cf2  [0.01958209 0.        ]\cf0 \
\cf2  [0.01925294 0.        ]\cf0 \
\cf2  [0.03872289 0.        ]\cf0 \
\cf2  [0.01852869 0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.02054691 0.        ]\cf0 \
\cf2  [0.01168833 0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.02330663 0.        ]\cf0 \
\cf2  [0.01594704 0.        ]\cf0 \
\cf2  [0.02994411 0.        ]\cf0 \
\cf2  [0.04717568 0.        ]\cf0 \
\cf2  [0.05394448 0.02537037]\cf0 \
\cf2  [0.01801949 0.        ]\cf0 \
\cf2  [0.05725785 0.        ]\cf0 \
\cf2  [0.05265245 0.        ]]\cf0 \
\
\cf2 Runtime: (13109+12144+19508+14087+9315+10878+14427)/7=13,352.571 \cf0 \
\
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 13109.558340549469 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 13144.874720096588 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 19508.89002251625 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 14087.850795030594 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 9315.919214487076 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 10878.27270770073 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.05, 0.05, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 14427.238541841507 seconds\cf0 \
\pard\pardeftab720\partightenfactor0

\f2\fs24 \AppleTypeServices\AppleTypeServicesF65539 \cf2 \
\
\pard\pardeftab720\partightenfactor0

\f0\fs22 \AppleTypeServices \cf2 _________________________\cf0 \
\cf2 Config 26: (Avg. runtime: 12759)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.69634509],[0.30365491]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04245621 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.51184005],[0.48815995]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02560067 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.45519717],[0.54480283]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0338799  0.03914926]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.53409462],[0.46590538]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09643338 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.63045791],[0.36954209]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04599581 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.62272778],[0.37727222]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02665484 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.46643803],[0.53356197]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0759903  0.04269876]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.58318981],[0.41681019]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09089182 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.60817387],[0.39182613]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05383622 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.63050242],[0.36949758]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04965717 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.53594071],[0.46405929]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06623069 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.60774703],[0.39225297]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.029502 0.      ]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.55806655],[0.44193345]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.035064 0.      ]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.40149262],[0.59850738]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03549282 0.05784074]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.59639556],[0.33208925],[0.07151518]). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.41025113],[0.58974887]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03150284 0.06255101]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.58032626],[0.41967374]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02797134 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.69514388],[0.30485612]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04163316 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.54294049],[0.45705951]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09254617 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.59674337],[0.40325663]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07476513 0.        ]\cf0 \
\cf2 True s1: 0.07, True s2: 0.09, True nns: 2, True rec rate: 2.5, hap/dip-loid: 5.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [1. 1. 2. 1. 1. 1. 2. 1. 1. 1. 1. 1. 1. 2. 0. 2. 1. 1. 1. 1.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.04245621 0.        ]\cf0 \
\cf2  [0.02560067 0.        ]\cf0 \
\cf2  [0.0338799  0.03914926]\cf0 \
\cf2  [0.09643338 0.        ]\cf0 \
\cf2  [0.04599581 0.        ]\cf0 \
\cf2  [0.02665484 0.        ]\cf0 \
\cf2  [0.0759903  0.04269876]\cf0 \
\cf2  [0.09089182 0.        ]\cf0 \
\cf2  [0.05383622 0.        ]\cf0 \
\cf2  [0.04965717 0.        ]\cf0 \
\cf2  [0.06623069 0.        ]\cf0 \
\cf2  [0.029502   0.        ]\cf0 \
\cf2  [0.035064   0.        ]\cf0 \
\cf2  [0.03549282 0.05784074]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.03150284 0.06255101]\cf0 \
\cf2  [0.02797134 0.        ]\cf0 \
\cf2  [0.04163316 0.        ]\cf0 \
\cf2  [0.09254617 0.        ]\cf0 \
\cf2  [0.07476513 0.        ]]\cf0 \
\
\cf2 Runtime: (11003+10181+15512+12017+13819+16511+7377+13028+15383)/9=12,759 \cf0 \
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 11003.214908599854 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 10181.391799211502 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 15512.612529754639 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 12017.86879658699 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 13819.054708242416 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 16511.91772723198 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 7377.507606506348 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 13028.472605466843 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.07, 0.09, 2, 2.5, 5.0, 20.0]\cf0 \
\
\cf2 Inference completed in 15383.16268658638 seconds\cf0 \
\cf2 _________________________\cf0 \
\cf2 Config 27: (Avg. runtime: 15,020.375)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.37737107],[0.62262893]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03664374 0.04539631]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.52191763],[0.47808237]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07581295 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.59608616],[0.40391384]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02896646 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.33856926],[0.66143074]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08912972 0.04128667]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.57357292],[0.42642708]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0496904 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.18767508],[0.81232492]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10716315 0.05321406]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.61622389],[0.34815668],[0.03561943]). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.49953149],[0.50046851]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02225728 0.09807857]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.6321759],[0.3678241]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10780981 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.67259312],[0.32740688]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05590936 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.44160471],[0.55839529]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05188721 0.05444488]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.46656983],[0.53343017]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.02642051 0.05070877]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.19426491],[0.80573509]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03603794 0.05150687]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.51715323],[0.48284677]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.11175439 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.68671906],[0.31328094]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.11057075 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.69714651],[0.30285349]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.05622509 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.66517708],[0.33482292]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0955869 0.       ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.39519864],[0.60480136]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03394228 0.04384488]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.48045943],[0.51954057]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.0479038  0.03624559]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.501323],[0.498677]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04439191 0.        ]\cf0 \
\cf2 True s1: 0.09, True s2: 0.12, True nns: 2, True rec rate: 2.5, hap/dip-loid: 5.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [2. 1. 1. 2. 1. 2. 0. 2. 1. 1. 2. 2. 2. 1. 1. 1. 1. 2. 2. 1.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.03664374 0.04539631]\cf0 \
\cf2  [0.07581295 0.        ]\cf0 \
\cf2  [0.02896646 0.        ]\cf0 \
\cf2  [0.08912972 0.04128667]\cf0 \
\cf2  [0.0496904  0.        ]\cf0 \
\cf2  [0.10716315 0.05321406]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.02225728 0.09807857]\cf0 \
\cf2  [0.10780981 0.        ]\cf0 \
\cf2  [0.05590936 0.        ]\cf0 \
\cf2  [0.05188721 0.05444488]\cf0 \
\cf2  [0.02642051 0.05070877]\cf0 \
\cf2  [0.03603794 0.05150687]\cf0 \
\cf2  [0.11175439 0.        ]\cf0 \
\cf2  [0.11057075 0.        ]\cf0 \
\cf2  [0.05622509 0.        ]\cf0 \
\cf2  [0.0955869  0.        ]\cf0 \
\cf2  [0.03394228 0.04384488]\cf0 \
\cf2  [0.0479038  0.03624559]\cf0 \
\cf2  [0.04439191 0.        ]]\cf0 \
\
\cf2 Runtime: (16344+13142+21714+12321+19505+11875+7556+17706)/8=15,020.375 \cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 16344.925879955292 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 13142.056129217148 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 21714.310749053955 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 12321.643267393112 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 19505.85820388794 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 11875.873126745224 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 7556.8807055950165 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.09, 0.12, 2, 2.5, 5.0, 20.0]\cf0 \
\
\cf2 Inference completed in 17706.132071495056 seconds\cf0 \
\cf2 _________________________\cf0 \
\cf2 Config 28: (Avg. runtime: 13792)\cf0 \
\cf2 Analysing now inferred posterior for 1-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.44292931],[0.55707069]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.03766825 0.05593482]\cf0 \
\cf2 Analysing now inferred posterior for 2-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.57178791],[0.42821209]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08349038 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 3-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.73705591],[0.23235272],[0.03059137]). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 4-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.43743015],[0.56256985]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.17676404 0.04012904]\cf0 \
\cf2 Analysing now inferred posterior for 5-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.87900222],[0.12099778],0). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 6-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.56647423],[0.43352577]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09812044 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 7-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.21005592],[0.78994408]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06722374 0.06383144]\cf0 \
\cf2 Analysing now inferred posterior for 8-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.51719512],[0.48280488]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.14983146 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 9-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.34748757],[0.65251243]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.11119965 0.10962335]\cf0 \
\cf2 Analysing now inferred posterior for 10-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = ([0.51548641],[0.42979918],[0.05471441]). Inferred nns: 0.0\cf0 \
\cf2 Analysing now inferred posterior for 11-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.52931377],[0.47068623]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09204521 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 12-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.44111285],[0.55888715]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.08512954 0.15238622]\cf0 \
\cf2 Analysing now inferred posterior for 13-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.35766917],[0.64233083]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10863066 0.08992944]\cf0 \
\cf2 Analysing now inferred posterior for 14-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.28280141],[0.71719859]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.04298059 0.05844981]\cf0 \
\cf2 Analysing now inferred posterior for 15-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.5037949],[0.4962051]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.17814734 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 16-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.3057276],[0.6942724]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.07872124 0.07294501]\cf0 \
\cf2 Analysing now inferred posterior for 17-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.54848127],[0.45151873]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.10744787 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 18-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.37238579],[0.62761421]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.06674456 0.0860606 ]\cf0 \
\cf2 Analysing now inferred posterior for 19-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.592632],[0.407368]). Inferred nns: 1.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.09249091 0.        ]\cf0 \
\cf2 Analysing now inferred posterior for 20-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 marginal posterior probability: (p0, p1, p2) = (0,[0.43659777],[0.56340223]). Inferred nns: 2.0\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [0.1287617  0.01904122]\cf0 \
\cf2 True s1: 0.14, True s2: 0.18, True nns: 2, True rec rate: 2.5, hap/dip-loid: 5.0, number of replicates: 20.0\cf0 \
\cf2 Inferred nns: [2. 1. 0. 2. 0. 1. 2. 1. 2. 0. 1. 2. 2. 2. 1. 2. 1. 2. 1. 2.]\cf0 \
\cf2 Conditional posterior mode of (s1, s2): [[0.03766825 0.05593482]\cf0 \
\cf2  [0.08349038 0.        ]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.17676404 0.04012904]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.09812044 0.        ]\cf0 \
\cf2  [0.06722374 0.06383144]\cf0 \
\cf2  [0.14983146 0.        ]\cf0 \
\cf2  [0.11119965 0.10962335]\cf0 \
\cf2  [0.         0.        ]\cf0 \
\cf2  [0.09204521 0.        ]\cf0 \
\cf2  [0.08512954 0.15238622]\cf0 \
\cf2  [0.10863066 0.08992944]\cf0 \
\cf2  [0.04298059 0.05844981]\cf0 \
\cf2  [0.17814734 0.        ]\cf0 \
\cf2  [0.07872124 0.07294501]\cf0 \
\cf2  [0.10744787 0.        ]\cf0 \
\cf2  [0.06674456 0.0860606 ]\cf0 \
\cf2  [0.09249091 0.        ]\cf0 \
\cf2  [0.1287617  0.01904122]]\cf0 \
\
\cf2 Runtime: (10833+13268+17188+14517+12854+12451+13860+15184+13979)/9 = 13792.667 \cf0 \
\cf2 Inference now running for 11-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 10833.281975030899 seconds\cf0 \
\cf2 Inference now running for 12-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 13268.20687508583 seconds\cf0 \
\cf2 Inference now running for 13-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 17188.697821617126 seconds\cf0 \
\cf2 Inference now running for 14-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 14517.320848703384 seconds\cf0 \
\cf2 Inference now running for 15-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 12854.15095615387 seconds\cf0 \
\cf2 Inference now running for 16-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 12451.845816135406 seconds\cf0 \
\cf2 Inference now running for 17-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 13860.296763181686 seconds\cf0 \
\cf2 Inference now running for 18-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\cf2 Inference completed in 15184.51691699028 seconds\cf0 \
\cf2 Inference now running for 19-st/nd/th replicate fakedata of configuration:[0.14, 0.18, 2, 2.5, 5.0, 20.0]\cf0 \
\
\cf2 Inference completed in 13979.113796710968 seconds\cf0 \
}