Generating Dependence Structure of Multiply Sectioned Bayesian Networks

Y. Xiang and X. An, University of Guelph, Canada

Multiply sectioned Bayesian networks(MSBNs) provide a general and exact framework for multi-agent distributed interpretation. To investigate algorithms for inference and other operations, experimental MSBNs are necessary. However, it is very time consuming and tedious to construct MSBNs mannually. In this work, we investigate pseudo-random generation of MSBNs. Our focus is on the generation of MSBN structures. Pseduo-random generation of MSBN structures can be performed by a generate-and-test approach. We expect such approach to have a very low probability of generating legal MSBN structures that satisfy all the technical constraints, and hence will be inefficient. We propose a set of algorithms that always generates legal MSBN dependence structures.

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