A recovery algorithm for chain graphs
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.
recovery algorithm chain graph markov equivalence dependency model pattern largest chain graph