Probability Update: Conditioning vs. Cross-Entropy
  Conditioning is the generally agreed-upon method for updating probability
distributions when one learns that an event is certainly true. But it has been
argued that we need other rules, in particular the rule of cross-entropy
minimization, to handle updates that involve uncertain information. In this
paper we re-examine such a case: van Fraassen's Judy Benjamin problem, which in
essence asks how one might update given the value of a conditional probability.
We argue that -- contrary to the suggestions in the literature -- it is
possible to use simple conditionalization in this case, and thereby obtain
answers that agree fully with intuition. This contrasts with proposals such as
cross-entropy, which are easier to apply but can give unsatisfactory answers.
Based on the lessons from this example, we speculate on some general
philosophical issues concerning probability update.
