 One more point about prior probabilities and Bayesian convergence should be mentioned before proceeding to  Section 4.  Some subjectivist versions of Bayesian induction seem to suggest that an agent’s prior plausibility assessments for hypotheses should stay fixed once-and-for-all, and that all plausibility updating should be brought about via the likelihoods in accord with Bayes’ Theorem. Critics argue that this is unreasonable. The members of a scientific community may quite legitimately revise their (comparative) prior plausibility assessments for hypotheses from time to time as they rethink plausibility arguments and bring new considerations to bear. This seems a natural part of the conceptual development of a science. It turns out that such reassessments of the comparative plausibilities of hypotheses poses no difficulty for the probabilistic inductive logic discussed here. Such reassessments may be represented by the addition or modification of explicit statements that modify the background information b. Such reassessments may result in (non-Bayesian) transitions to new vagueness sets for individual agents and new diversity sets for the community. The logic of Bayesian induction (as described here) has nothing to say about what values the prior plausibility assessments for hypotheses should have; and it places no restrictions on how they might change over time. Provided that the series of reassessments of (comparative) prior plausibilities doesn’t happen to diminish the (comparative) prior plausibility value of the true hypothesis towards zero (or, at least, doesn’t do so too quickly), the Likelihood Ratio Convergence Theorem implies that the evidence will very probably bring the posterior probabilities of empirically distinct rivals of the true hypothesis to approach 0 via decreasing likelihood ratios; and as this happens, the posterior probability of the true hypothesis will head towards 1.