A recovery algorithm for chain graphs

Milan Studený

doi:10.1016/S0888-613X(97)00018-2

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A recovery algorithm for chain graphs

1997-08-01

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Milan Studený *

DOI：10.1016/S0888-613X(97)00018-2

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Abstract

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The class of chain graphs (CGs) involving both undirected graphs (=Markou networks) and directed acyclic graphs (= Bayesian networks) was introduced in middle eighties for description of probabilistic conditional independence structures. Every class of Markov equivalent CGs (that is, CGs describing the same conditional independence structure) has a natural representative which is called the largest CG. The paper presents a recovery algorithm which on the basis of the conditional independence structure given by a CG (in the form of a dependency model) finds the largest CG representing the corresponding class of Markov equivalent CGs. As a by-product a graphical characterization of graphs which are the largest CGs (for a class of Markov equivalent CGs) is obtained, and a simple algorithm changing every CG into the largest CG of the corresponding equivalence class is given. (C) 1997 Elsevier Science Inc.

Keywords:

chain graph

dependency model

Markov equivalence

pattern

largest chain graph

recovery algorithm