Path Propagation for Inference in Bayesian Networks

Authors:
Dan Wu;Liu He
Affiliations:
School of Computer Science, University of Windsor, Windsor Ontario, N9B 3P4, Canada;School of Computer Science, University of Windsor, Windsor Ontario, N9B 3P4, Canada
Venue:
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Year:
2007

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Abstract

Although (probabilistic) inference in Bayesian networks has been well studied, the recent trend on extending Bayesian networks to model large and complex domains imposes new challenges on inference. In this paper, we suggest a method called path propagation that addresses these new challenges. The experimental results indicate that the proposed method achieves better performance than conventional method, especially for large Bayesian networks.