A combination of exact algorithms for inference on Bayesian belief networks

10Citations
Citations of this article
13Readers
Mendeley users who have this article in their library.
Get full text

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. © 1991.

Cite

CITATION STYLE

APA

Suermondt, H. J., & Cooper, G. F. (1991). A combination of exact algorithms for inference on Bayesian belief networks. International Journal of Approximate Reasoning, 5(6), 521–542. https://doi.org/10.1016/0888-613X(91)90028-K

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free