Comparing Alternative Methods for Inference in Multiply Sectioned Bayesian Networks

Y. Xiang

Multiply sectioned Bayesian networks (MSBNs) provide one framework for agents to estimate the state of a domain. Existing methods for multi-agent inference in MSBNs are based on linked junction forests (LJFs). The methods are extensions of message passing in junction trees for inference in single-agent Bayesian networks (BNs). We consider extending other inference methods in single-agent BNs to multi-agent inference in MSBNs. In particular, we consider distributed versions of loop cutset conditioning and forward sampling. They are compared with the LJF method in terms of off-line compilation, inter-agent messages during communication, consistent local inference, and preservation of agent privacy.


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