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The marginal factorization of Bayesian networks and its application
Author(s) -
Wu Dan,
Wong Michael
Publication year - 2004
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20024
Subject(s) - directed acyclic graph , bayesian network , factorization , conditional probability , bayesian probability , computer science , chain rule (probability) , joint probability distribution , marginal distribution , set (abstract data type) , mathematics , directed graph , theoretical computer science , algorithm , artificial intelligence , posterior probability , regular conditional probability , random variable , statistics , programming language
A Bayesian network consists of a directed acyclic graph (DAG) and a set of conditional probability distributions (CPDs); they together define a joint probability distribution (jpd). The structure of the DAG dictates how a jpd can be factorized as a product of CPDs. This CPD factorization view of Bayesian networks has been well recognized and studied in the uncertainty community. In this article, we take a different perspective by studying a marginal factorization view of Bayesian networks. In particular, we propose an algebraic characterization of equivalent DAGs based on the marginal factorization of a jpd defined by a Bayesian network. Moreover, we show a simple method to identify all the compelled edges in a DAG. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 769–786, 2004.
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