A standard approach for optimizing belief network inference using query DAGs

Authors:
Adnan Darwiche;Gregory Provan
Affiliations:
Department of Mathematics, American University of Beirut, Beirut, Lebanon;Department of Diagnostics, Rockwell Science Center, Thousand Oaks, Ca
Venue:
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Year:
1997

Citing 2
Cited 2

Query DAGs: a practical paradigm for implementing belief-network inference

Journal of Artificial Intelligence Research
Query DAGs: a practical paradigm for implementing belief-network inference

UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence

Efficient reasoning

ACM Computing Surveys (CSUR)
Efficient indexing methods for recursive decompositions of Bayesian networks

International Journal of Approximate Reasoning

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Abstract

This paper proposes a novel, algorithm-independent 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 significantly simplify the process of designing algorithms for optimizing belief network inference.