The marginal factorization of Bayesian networks and its application

✍ Scribed by Dan Wu; Michael Wong

• Publisher: John Wiley and Sons
• Year: 2004
• Tongue: English
• Weight: 162 KB
• Volume: 19
• Category: Article
• ISSN: 0884-8173
• DOI: 10.1002/int.20024

✦ Synopsis

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.