Compiling Relational Bayesian Networks for Exact Inference

We describe 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 the 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.