On the Markov Equivalence of Chain Graphs, Undirected Graphs, and Acyclic Digraphs
Steen Arne Andersson, David Madigan, Michael D. Perlman · Scandinavian Journal of Statistics · 1997
Graphical Markov models use undirected graphs (UDGs), acyclic directed graphs (ADGs), or (mixed) chain graphs to represent possible dependencies among random variables in a multivariate distribution. Whereas a UDG is uniquely determined by its associated Markov model, this is not true for ADGs or for general chain graphs (which include both UDGs and ADGs as special cases). This paper addresses three questions regarding the equivalence of graphical Markov models: when is a given chain graph Markov equivalent (1) to some UDG? (2) to some (at least one) ADG? (3) to some decomposable UDG? The answers are obtained by means of an extension of Frydenberg’s (1990) elegant graph‐theoretic characterization of the Markov equivalence of chain graphs.