On the complexity of probabilistic inference in singly connected bayesian networks

Authors:
Dan Wu;Cory Butz
Affiliations:
School of Computer Science, University of Windsor, Windsor, Ontario, Canada;Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada
Venue:
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Year:
2005

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Abstract

In this paper, we revisit the consensus of computational complexity on exact inference in Bayesian networks. We point out that even in singly connected Bayesian networks, which conventionally are believed to have efficient inference algorithms, the computational complexity is still NP-hard.