Generating Dependence Structure of Multiply Sectioned Bayesian Networks
Yang Xiang, Xiangdong An · 2001
Multiply sectioned Bayesian networks (MSBNs) pro-vide a general and exact framework for multi-agent dis-tributed interpretation. To investigate algorithms for inference and other operations, experimental MSBNs are necessary. However, it is very time consuming and tedious to construct MSBNs manually. In this work, we investigate pseduo-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 ex-pect such approach to have a very low probability of generating legal MSBN structures that satisfy all the technical constraints, and hence will be ineÆcient. We propose a set of algorithms that always generates legal MSBN dependence structures. 1