Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach

This paper presents generalizations of Bayes likelihood-ratio updating rule which facilitate an asynchronous propagation of the impacts of new beliefs and/or new evidence in hierarchically organized inference structures with multi-hypotheses variables.

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• 1 The computational scheme proposed specifies a set of belief parameters, communication messages and updating rules which guarantee that the diffusion of updated beliefs is accomplished in a single pass and complies with the tenets of Bayes calculus.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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