Computational Advantages of Relevance Reasoning in Bayesian Belief Networks

Abstract

This paper introduces a computational framework for reasoning in Bayesian belief networks that derives significant advantages from focused inference and relevance reasoning. This framework is based on d -separation and other simple and computationally efficient techniques for pruning irrelevant parts of a network. Our main contribution is a technique that we call relevance-based decomposition. Relevance-based decomposition approaches belief updating in large networks by focusing on their parts and decomposing them into partially overlapping subnetworks. This makes reasoning in some intractable networks possible and, in addition, often results in significant speedup, as the total time taken to update all subnetworks is in practice often considerably less than the time taken to update the network as a whole. We report results of empirical tests that demonstrate practical significance of our approach.

Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997)

References in corpus (3)

• Context-Specific Independence in Bayesian Networks
• A Sufficiently Fast Algorithm for Finding Close to Optimal Junction Trees
• Pruning Bayesian Networks for Efficient Computation

Cited by in corpus (5)

• Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams)
• Lazy Propagation in Junction Trees
• Constructing Situation Specific Belief Networks
• Hypothesis Management in Situation-Specific Network Construction
• Pivotal Pruning of Trade-offs in QPNs