 This requires some clarification. For what could it mean for a Bayesian to be an explanationist? In order to apply Bayes’ rule and determine the probability for H after learning E, the Bayesian agent will have to determine the probability of H conditional on E. For that, he needs to assign unconditional probabilities to H and E as well as a probability to E given H; the former two are mostly called “prior probabilities” (or just “priors”) of, respectively, H and E, the latter the “likelihood” of H on E. (This is the official Bayesian story. Not all of those who sympathize with Bayesianism adhere to that story. For instance, according to some it is more reasonable to think that conditional probabilities are basic and that we derive unconditional probabilities from them; see Hájek 2003, and references therein.) How is the Bayesian to determine these values? As is well known, probability theory gives us more probabilities once we have some; it does not give us probabilities from scratch. Of course, when H implies E or the negation of E, or when H is a statistical hypothesis that bestows a certain chance on E, then the likelihood follows “analytically.” (This claim assumes some version of Lewis’ (1980) Principal Principle, and it is controversial whether or not this principle is analytic; hence the scare quotes.) But this is not always the case, and even if it were, there would still be the question of how to determine the priors. This is where, according to Lipton, abduction comes in. In his proposal, Bayesians ought to determine their prior probabilities and, if applicable, likelihoods on the basis of explanatory considerations.