Unifying Markov Properties for Graphical Models

TL;DR

This paper introduces a unified framework for various Markov properties in graphical models by defining a new class of graphs with four edge types and a single separation criterion, unifying multiple existing models.

Contribution

It proposes a new class of graphs with four edge types and a unified separation criterion, unifying previous Markov property definitions and establishing equivalence with existing models.

Findings

Unified Markov properties through a new graph class.

Equivalence of pairwise and global Markov properties for certain models.

Compatibility with existing graphical model subclasses.

Abstract

Several types of graphs with different conditional independence interpretations --- also known as Markov properties --- have been proposed and used in graphical models. In this paper we unify these Markov properties by introducing a class of graphs with four types of edges --- lines, arrows, arcs, and dotted lines --- and a single separation criterion. We show that independence structures defined by this class specialize to each of the previously defined cases, when suitable subclasses of graphs are considered. In addition, we define a pairwise Markov property for the subclass of chain mixed graphs which includes chain graphs with the LWF interpretation, as well as summary graphs (and consequently ancestral graphs). We prove the equivalence of this pairwise Markov property to the global Markov property for compositional graphoid independence models.