A Standard Approach for Optimizing Belief Network Inference Using Query DAGs

Query DAGs Adnan Darwiche Department of Mathematics American University of Beirut PO Box 11 236 Beirut, Lebanon [email protected] Gregory Provan Department of Diagnostics Rockwell Science Center 1049 Camino Dos Rios Thousand Oaks, Ca 91360 [email protected] Abstract This paper proposes a novel, algorithmindependent approach to optimizing belief network inference. Rather than designing optimizations on an algorithm by algorithm basis, we argue that one should use an unoptimized algorithm to generate a Q-DAG, a compiled graphical representation of the belief network, and then optimize the Q-DAG and its evaluator instead. We present a set of Q-DAG optimizations that supplant optimizations designed for traditional inference algorithms, including zero compression, network pruning and caching. We show that our Q-DAG optimizations require time linear in the Q-DAG size, and signi cantly simplify the process of designing algorithms for optimizing belief network inference.