We study the problem of learning the probability distribution of a multinomial variable from an observed sequence of signals, starting in a condition of ignorance about this distribution. We show that not all signals are suited for producing non-vacuous inferences under prior ignorance. To...

The imprecise Beta model (IBM) of Bernard (1996) and Walley (1996) is the most popular model for learning about a binomial random variable under prior ignorance. Piatti et al. (2005) show that there is a fundamental issue with the interpretation of results produced by the IBM in applications....