Factorization, Inference and Parameter Learning in Discrete AMP Chain Graphs

TL;DR

This paper demonstrates how discrete AMP chain graphs can be factorized for efficient inference and learning, and provides an intuitive interpretation of their edges, addressing practical and interpretative challenges.

Contribution

It introduces a factorization method for discrete AMP chain graphs, enabling efficient inference and parameter learning, and offers an intuitive interpretation of their edges.

Findings

Factorization of distributions satisfying AMP chain graph independencies

Adaptation of algorithms for inference and learning in Bayesian networks

Provision of an intuitive edge interpretation for AMP chain graphs

Abstract

We address some computational issues that may hinder the use of AMP chain graphs in practice. Specifically, we show how a discrete probability distribution that satisfies all the independencies represented by an AMP chain graph factorizes according to it. We show how this factorization makes it possible to perform inference and parameter learning efficiently, by adapting existing algorithms for Markov and Bayesian networks. Finally, we turn our attention to another issue that may hinder the use of AMP CGs, namely the lack of an intuitive interpretation of their edges. We provide one such interpretation.