Probabilities of Hypotheses and Information-Statistics in Sampling from Exponential-Class Populations

Abstract

This paper is concerned with inequalities connecting probabilities of hypotheses using Bayes' theorem (a posteriori probabilities), a priori probabilities, and Kullback-Leibler information-statistics in sampling from populations belonging to the exponential class of populations. As a corollary, it is shown that if it is known that the a priori probabilities are all equal, the choice of the hypothesis with the minimum Kullback-Leibler information-statistic is the same as the choice of the hypothesis with the maximum a posteriori probability, and conversely.