The efficient propagation of arbitrary subsets of beliefs in discrete-valued Bayesian belief networks

摘要

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.