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International Journal of Approximate Reasoning
Volume 5, Issue 6, November 1991, Pages 521-542
 
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doi:10.1016/0888-613X(91)90028-K    How to Cite or Link Using DOI (Opens New Window)
Copyright © 1991 Published by Elsevier Science Inc.

A combination of exact algorithms for inference on Bayesian belief networks

H. Jacques SuermondtCorresponding Author Contact Information and Gregory F. Cooper*

Medical Computer Science Group Knowledge Systems Laboratory Stanford University, Stanford, California, USA

Received 2 January 1990; 
accepted 4 December 1991. ;
Available online 20 May 2003.

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Abstract

Cutset conditioning and clique-tree propagation are two popular methods for exact probabilistic inference in Bayesian belief networks. Cutset conditioning is based on decomposition of a subset network nodes, whereas clique-tree propagation depends on aggregation of nodes. We characterize network structures in which the performances of these methods differ. We describe a means to combine cutset conditioning and clique-tree propagation in an approach called aggregation after decomposition (AD), which can perform inference relatively efficiently for certain network structures in which neither cutset conditioning nor clique-tree propagation performs well. We discuss criteria to determine when AD will perform more efficient belief-network inference than will clique-tree propagation.

Author Keywords: probabilistic reasoning; belief networks; artificial intelligence; Bayesian methods; reasoning under uncertainty; expert systems

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