Copyright © 1991 Published by Elsevier Science Inc.
A combination of exact algorithms for inference on Bayesian belief networks
Received 2 January 1990;
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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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