Thumbnail
Access Restriction
Open

Author Studeny, Milan
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Recovery Algorithm ♦ Chain Graph ♦ Separation Criterion ♦ Bayesian Network ♦ Dependency Model ♦ Probabilistic Conditional Independence Structure Use ♦ Introduction Traditional Graphical Model ♦ Direct Graphical Separation Criterion ♦ Classic Moralization Criterion ♦ Every Class ♦ Markov Equivalent Cgs ♦ Graphical Model ♦ Probability Distribution ♦ D-separation Criterion ♦ Natural Unifying Point ♦ Undirected Graph ♦ Conditional Independence Structure ♦ Natural Representative
Abstract Chain graphs (CGs) give a natural unifying point of view on Markov and Bayesian networks and enlarge the potential of graphical models for description of conditional independence structures. In the paper a direct graphical separation criterion for CGs which generalizes the d-separation criterion for Bayesian networks is introduced (recalled) . It is equivalent to the classic moralization criterion for CGs and complete in the sense that for every CG there exists a probability distribution satisfying exactly independencies derivable from the CG by the separation criterion. Every class of Markov equivalent CGs can be uniquely described by a natural representative, called the largest CG. A recovery algorithm, which on basis of the (conditional) dependency model given by a CG finds the corresponding largest CG, is presented. 1 INTRODUCTION Traditional graphical models for description of probabilistic conditional independence structure use either undirected graphs (UGs), named also Markov n...
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Publisher Date 1996-01-01