Large Deviations For Randomly Weighted Sums of Random Measures

TL;DR

This paper establishes large deviation principles for sums of random probability measures weighted by random vectors, providing explicit rate functions and applications to Dirichlet means and divergence measures.

Contribution

It introduces explicit large deviation results for randomly weighted sums of measures, including Dirichlet cases, and links them to divergence and entropy minimization.

Findings

Explicit rate function for finite Dirichlet weighted sums.

Large deviation principles for Dirichlet and posterior means.

Identification of entropy minima under mean constraints.

Abstract

Let $\{{\bf \mathcal{Z}}_n:n\geq 1\}$ be a sequence of i.i.d. random probability measures. Independently, for each $n\geq 1$, let $(X_{n1},\ldots, X_{nn})$ be a random vector of positive random variables that add up to one. This paper studies the large deviation principles for the randomly weighted sum $\sum_{i=1}^{n} X_{ni} \mathcal{Z}_i$. In the case of finite Dirichlet weighted sum of Dirac measures, we obtain an explicit form for the rate function. It provides a new measurement of divergence between probabilities. As applications, we obtain the large deviation principles for a class of randomly weighted means including the Dirichlet mean and the corresponding posterior mean. We also identify the minima of relative entropy with mean constraint in both forward and reverse directions.