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That just explains Bayesian inference in general, which is useful, but not what I'm after.

I was specifically interested in the application to binary search / bisection in the presence of flaky tests.



You apply the same process, but your hypotheses are "bug is before/after this bisection point". Your "probability of evidence given hypothesis before/after" are where you incorporate your guess about the tests flakiness. Still works even if you don't have "true" numbers for the tests flakiness, just won't converge as quickly




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