The Efficient Propagation of Arbitrary Subsets of Beliefs in Discrete-Valued Bayesian Networks
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, PMLR R3:272-277, 2001.
The paper describes an approach for propagating arbitrary subsets of beliefs in Bayesian Belief Networks. The method is based on a multiple message passing scheme in junction trees. A hybrid tree structure is introduced, both for the propagation of evidence and as an efficiently permutable representation of a decomposable graph. The use of maximal prime subgraph decompositions and tree permutations to reduce computational cost is demonstrated.