Graphical Markov models for infinitely many variables

TL;DR

This paper extends the theory of graphical models to infinite-dimensional systems, establishing conditions under which conditional independences can be represented by graphs, and demonstrating their applicability in infinite-variable contexts.

Contribution

It provides a rigorous framework for representing conditional independences in infinite graphical models, generalizing finite case results and addressing challenges unique to infinite dimensions.

Findings

Naive extensions of finite models do not preserve equivalence of Markov properties in infinite cases.

Derived general conditions enable graph-based representation of conditional independence in infinite systems.

Applied theory to concrete examples of infinite-dimensional graphical models.

Abstract

Representing the conditional independences present in a multivariate random vector via graphs has found widespread use in applications, and such representations are popularly known as graphical models or Markov random fields. These models have many useful properties, but their fundamental attractive feature is their ability to reflect conditional independences between blocks of variables through graph separation, a consequence of the equivalence of the pairwise, local and global Markov properties demonstrated by Pearl and Paz (1985). Modern day applications often necessitate working with either an infinite collection of variables (such as in a spatial-temporal field) or approximating a large high-dimensional finite stochastic system with an infinite-dimensional system. However, it is unclear whether the conditional independences present in an infinite-dimensional random vector or stochastic process can still be represented by separation criteria in an infinite graph. In light of the advantages of using graphs as tools to represent stochastic relationships, we undertake in this paper a general study of infinite graphical models. First, we demonstrate that naive extensions of the assumptions required for the finite case results do not yield equivalence of the Markov properties in the infinite-dimensional setting, thus calling for a more in-depth analysis. To this end, we proceed to derive general conditions which do allow representing the conditional independence in an infinite-dimensional random system by means of graphs, and our results render the result of Pearl and Paz as a special case of a more general phenomenon. We conclude by demonstrating the applicability of our theory through concrete examples of infinite-dimensional graphical models.