Exploring Localization in Bayesian Networks for Large Expert Systems

Yang Xiang; David L. Poole; Michael P. Beddoes

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arXiv · 1303.5438

Exploring Localization in Bayesian Networks for Large Expert Systems

Yang Xiang·David L. Poole·Michael P. Beddoes

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Abstract

Current Bayesian net representations do not consider structure in the domain and include all variables in a homogeneous network. At any time, a human reasoner in a large domain may direct his attention to only one of a number of natural subdomains, i.e., there is ?localization' of queries and evidence. In such a case, propagating evidence through a homogeneous network is inefficient since the entire network has to be updated each time. This paper presents multiply sectioned Bayesian networks that enable a (localization preserving) representation of natural subdomains by separate Bayesian subnets. The subnets are transformed into a set of permanent junction trees such that evidential reasoning takes place at only one of them at a time. Probabilities obtained are identical to those that would be obtained from the homogeneous network. We discuss attention shift to a different junction tree and propagation of previously acquired evidence. Although the overall system can be large, computational requirements are governed by the size of only one junction tree.

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BibTeX RIS

Yang Xiang, David L. Poole, Michael P. Beddoes. 2013-03-13. Exploring Localization in Bayesian Networks for Large Expert Systems. https://arxiv.org/abs/1303.5438

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