Learning Bayesian networks from data: An information-theory based approach

This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional indepe...

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Bibliographic Details
Published in:Artificial intelligence 2002-05, Vol.137 (1), p.43-90
Main Authors: Cheng, Jie, Greiner, Russell, Kelly, Jonathan, Bell, David, Liu, Weiru
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Bayesian belief nets
Bayesian networks
Conditional independence test
Data mining
Information theory
Knowledge discovery
Learning
Monotone DAG-faithful
Probabilistic model
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Summary:This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional independence (CI) tests in typical cases. We provide precise conditions that specify when these algorithms are guaranteed to be correct as well as empirical evidence (from real world applications and simulation tests) that demonstrates that these systems work efficiently and reliably in practice.
ISSN:0004-3702
1872-7921
DOI:10.1016/S0004-3702(02)00191-1