Weighted probabilistic opinion pooling based on cross-entropy

Abstract

In this work we focus on opinion pooling in the finite group of sources introduced in [1]. This approach, heavily exploiting Kullback-Leibler divergence (also known as cross-entropy), allows us to combine sources’ opinions given in probabilistic form, i.e. represented by the probability mass function (pmf). However, this approach assumes that sources are equally reliable with no preferences on, e.g., importance of a particular source. The discussion about the influence of the combination by preferences among sources (represented by weights) and numerical demonstration of the derived theory on an illustrative example form the core of this contribution.