DISTRIBUTED INFERENCE IN BAYESIAN NETWORKS

Bayesian networks originated as a framework for distributed reasoning. In singly connected networks, there exists an elegant inference algorithm that can be implemented in parallel having a processor for every node. It can be extended to take advantage of the OR-gate, a model of interaction among causes that simplifies knowledge acquisition and evidence propagation. We also discuss two exact and one approximate methods for dealing with general networks. It will be shown how all these algorithms admit distributed implementations.

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