Learning Bayesian Networks from Data: An Efficient Approach Based on Information Theory

Learning Bayesian Networks from Data: An Efficient Approach Based on Information Theory (1997)

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by Jie Cheng , David Bell , Weiru Liu

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@MISC{Cheng97learningbayesian,
    author = {Jie Cheng and David Bell and Weiru Liu},
    title = {Learning Bayesian Networks from Data: An Efficient Approach Based on Information Theory},
    year = {1997}
}

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Abstract

This paper addresses the problem of learning Bayesian network structures from data by using an information theoretic dependency analysis approach. Based on our three-phase construction mechanism, two efficient algorithms have been developed. One of our algorithms deals with a special case where the node ordering is given, the algorithm only require ) ( 2 N O CI tests and is correct given that the underlying model is DAG-Faithful [Spirtes et. al., 1996]. The other algorithm deals with the general case and requires ) ( 4 N O conditional independence (CI) tests. It is correct given that the underlying model is monotone DAG-Faithful (see Section 4.4). A system based on these algorithms has been developed and distributed through the Internet. The empirical results show that our approach is efficient and reliable. 1 Introduction The Bayesian network is a powerful knowledge representation and reasoning tool under conditions of uncertainty. A Bayesian network is a directed acyclic graph ...

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