Consistent Inference of Probabilities for Reproducible Experiments

Abstract

The need for inducing a probability distribution from partial data and the complementary problem of the analysis of an observed distribution in terms of fewer relevant variables occur in many branches of physics. For reproducible experiments, consistency conditions which must be satisfied by any algorithm for inferring a discrete probability distribution with given averages are formulated. The only consistent algorithm is the one leading to the distribution of maximal entropy subject to the given constraints.