Towards a faster inference algorithm in multiply sectioned Bayesian networks

Authors:
Karen H. Jin;Dan Wu
Affiliations:
School of Computer Science, University of Windsor, Windsor, Ontario, Canada;School of Computer Science, University of Windsor, Windsor, Ontario, Canada
Venue:
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Year:
2008

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Abstract

Multiply sectioned Bayesian network(MSBN) is an extension of Bayesian network(BN) model for the support of flexible modelling in large and complex problem domains. However, current MSBN inference methods involve extensive intra-subnet(internal) and inter-subnet (external) message passings. In this paper, we present a new MSBN message passing scheme which substantially reduces the total number of message passings. By saving on both internal and external messages, our method improves the overall efficiency of MSBN inference compared with existing methods.