Tree-structured Markov random fields with Poisson marginal distributions

TL;DR

This paper introduces a novel family of tree-structured Markov random fields with Poisson marginals, offering analytical tractability, efficient sampling, and scalable computation for high-dimensional count data applications.

Contribution

The paper presents a new family of Markov random fields with Poisson marginals, featuring explicit formulas and scalable methods, which is uncommon in existing models.

Findings

Analytic expressions for joint pmf and pgf

Efficient sampling procedures

Distributional analysis of sums and contributions

Abstract

A new family of tree-structured Markov random fields for a vector of discrete counting random variables is introduced. According to the characteristics of the family, the marginal distributions of the Markov random fields are all Poisson with the same mean, and are untied from the strength or structure of their built-in dependence. This key feature is uncommon for Markov random fields and most convenient for applications purposes. The specific properties of this new family confer a straightforward sampling procedure and analytic expressions for the joint probability mass function and the joint probability generating function of the vector of counting random variables, thus granting computational methods that scale well to vectors of high dimension. We study the distribution of the sum of random variables constituting a Markov random field from the proposed family, analyze a random variable's individual contribution to that sum through expected allocations, and establish stochastic orderings to assess a wide understanding of their behavior.