[rpier001]

Except that the quote reflects a common misunderstanding. The problem of optimal stopping is mostly a function of decision making over multiple looks. A Bayesian approach that makes decisions over multiple looks has similar issues. This can be mitigated by strong priors, but typically to an unknown degree and at some cost to 'power'. How/why does this claim arise? It is because a 'true' Bayesian approach makes no decisions/inferences - it just describes the current state of knowledge. If describing the distribution of the posterior, then 'stop at any time and be valid' else 'you got your Neyman-Pearson in my Bayesian analysis'.