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Reduce the default default channel bounds half-life
Utilizing the results of probes sent once a minute to a random node
in the network for a random amount (within a reasonable range), we
were able to analyze the accuracy of our resulting success
probability estimation with various PDFs across the historical and
live-bounds models.
For each candidate PDF (as well as other parameters, to be tuned in
the coming commits), we used the `min_zero_implies_no_successes`
fudge factor in `success_probability` as well as a total
probability multiple fudge factor to get both the historical
success model and the a priori model to be neither too optimistic
nor too pessimistic (as measured by the relative log-loss between
succeeding and failing hops in our sample data).
Across the simulation runs, for a given PDF and other parameters,
we nearly always did better with a shorter half-life (even as short
as 1ms, i.e. only learning per-probe rather than across probes).
While this likely makes sense for nodes which do live probing, not
all nodes do, and thus we should avoid over-biasing on the dataset
we have.
While it may make sense to only learn per-payment and not across
payments, I can't fully rationalize this result and thus want to
avoid over-tuning, so here we reduce the half-life from 6 hours to
30 minutes.
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