 Exactly how are explanatory considerations to guide one’s choice of priors? The answer to this question is not as simple as one might at first think. Suppose you are considering what priors to assign to a collection of rival hypotheses and you wish to follow Lipton’s suggestion. How are you to do this? An obvious—though still somewhat vague—answer may seem to go like this: Whatever exact priors you are going to assign, you should assign a higher one to the hypothesis that explains the available data best than to any of its rivals (provided there is a best explanation). Note, though, that your neighbor, who is a Bayesian but thinks confirmation has nothing to do with explanation, may well assign a prior to the best explanation that is even higher than the one you assign to that hypothesis. In fact, his priors for best explanations may even be consistently higher than yours, not because in his view explanation is somehow related to confirmation—it is not, he thinks—but, well, just because. In this context, “just because” is a perfectly legitimate reason, because any reason for fixing one’s priors counts as legitimate by Bayesian standards. According to mainstream Bayesian epistemology, priors (and sometimes likelihoods) are up for grabs, meaning that one assignment of priors is as good as another, provided both are coherent (that is, they obey the axioms of probability theory). Lipton’s recommendation to the Bayesian to be an explanationist is meant to be entirely general. But what should your neighbor do differently if he wants to follow the recommendation? Should he give the same prior to any best explanation that you, his explanationist neighbor, give to it, that is, lower his priors for best explanations? Or rather should he give even higher priors to best explanations than those he already gives?