fix tut 15

Author: janfb

tutorials/15_importance_sampled_posteriors.ipynb

@@ -58,11 +58,9 @@
     "from torch import ones, eye\n",
     "import torch\n",
     "from torch.distributions import MultivariateNormal\n",
-    "import matplotlib.pyplot as plt\n",
     "\n",
     "from sbi.inference import NPE, ImportanceSamplingPosterior\n",
     "from sbi.utils import BoxUniform\n",
-    "from sbi.inference.potentials.base_potential import BasePotential\n",
     "from sbi.analysis import marginal_plot"
    ]
   },
@@ -71,7 +69,7 @@
    "id": "4808d6d3-cb14-4ecd-a0f5-824bf54a5b01",
    "metadata": {},
    "source": [
-    "We first define a simulator and a prior which both have functions for sampling (as required for SBI) and log_prob evaluations (as required for importance sampling)."
+    "We first define a simulator and a prior which both have a `sample` function (as required for `sbi`) and `log_prob` evaluations (as required for importance sampling)."
    ]
   },
   {
@@ -202,9 +200,16 @@
    "id": "7150cc13-9911-4656-936f-9cafb867eba4",
    "metadata": {},
    "source": [
-    "With the SBI toolbox, importance sampling is a one-liner. SBI supports two methods for importance sampling:\n",
-    "- `\"importance\"`: returns `n_samples` weighted samples (as above) corresponding to `n_samples * sample_efficiency` samples from the posterior. This results in unbiased samples, but the number of effective samples may be small when the SBI estimate is inaccurate.\n",
-    "- `\"sir\"` (sampling-importance-resampling): performs rejection sampling on a batched basis with batch size `oversampling_factor`.  This is a guaranteed way to obtain `N / oversampling_factor` samples, but these may be biased as the weight normalization is not performed across the entire set of samples."
+    "With the `sbi` toolbox, importance sampling is a one-liner. `sbi` supports two methods\n",
+    "for importance sampling:\n",
+    "- `\"importance\"`: returns `n_samples` weighted samples (as above) corresponding to\n",
+    "  `n_samples * sample_efficiency` samples from the posterior. This results in unbiased\n",
+    "  samples, but the number of effective samples may be small when the `sbi` estimate is\n",
+    "  inaccurate.\n",
+    "- `\"sir\"` (sampling-importance-resampling): performs rejection sampling on a batched\n",
+    "  basis with batch size `oversampling_factor`.  This is a guaranteed way to obtain `N /\n",
+    "  oversampling_factor` samples, but these may be biased as the weight normalization is\n",
+    "  not performed across the entire set of samples."
    ]
   },
   {