Coherent Statistical Inference and Bayes Theorem

Abstract

Conditions are given which suffice for the assessment of a coherent inference by means of a Bayesian algorithm, i.e., a suitable extension of the classical Bayes theorem relative to a finite number of alternatives. Under some further hypotheses such inference is shown to be, in addition, coherent in the sense of Heath, Lane and Sudderth. Moreover, a characterization of coherent posteriors is provided, together with some remarks concerning finitely additive conditional probabilities.