probabilityFormula() is expected to return an error if capacity is 0, so
we should exclude that case from fuzzing.
Previously it was attempted to avoid this case by seeding the corpus
with an input that had capacity 1. That is not an effective solution
since the fuzzer can still generate an input with capacity 0.
The process how we calculate a total probability from the direct and
node probability is modified to give more importance to the direct
probability.
This is important if we know about a recent failure. We should not try
to send over this channel again if we know we can't. Otherwise this can
lead to infinite retrials over the channel, when the probability is
pinned at high probabilities by the other node results.
Implements a new probability estimator based on a probability theory
framework.
The computed probability consists of:
* the direct channel probability, which is estimated based on a
depleted liquidity distribution model, formulas and broader concept derived
after Pickhardt et al. https://arxiv.org/abs/2103.08576
* an extension of the probability model to incorporate knowledge decay
after time for previous successes and failures
* a mixed node probability taking into account successes/failures on other
channels of the node (similar to the apriori approach)
