Compiling relational Bayesian networks for exact inference

Authors:
Mark Chavira;Adnan Darwiche;Manfred Jaeger
Affiliations:
Computer Science Department, UCLA, Los Angeles, CA 90095, United States;Computer Science Department, UCLA, Los Angeles, CA 90095, United States;Institut for Datalogi, Aalborg Universitet, Fredrik Bajers Vej 7 E, DK-9220 Aalborg Ø, Denmark
Venue:
International Journal of Approximate Reasoning
Year:
2006

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Abstract

We describe in this paper a system for exact inference with relational Bayesian networks as defined in the publicly available Primula tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference by evaluating and differentiating these circuits in time linear in their size. We report on experimental results showing successful compilation and efficient inference on relational Bayesian networks, whose Primula-generated propositional instances have thousands of variables, and whose jointrees have clusters with hundreds of variables.