Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Structuring conditional relationships in influence diagrams
Operations Research
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Graph Algorithms
Evidence Absorption and Propagation through Evidence Reversals
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
d-Separation: From Theorems to Algorithms
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
An Introduction to Algorithms for Inference in Belief Nets
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Pruning bayesian networks for efficient computation
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
A sufficiently fast algorithm for finding close to optimal junction trees
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Qualitative reasoning under ignorance and information-relevant extraction
Knowledge and Information Systems
A review of explanation methods for Bayesian networks
The Knowledge Engineering Review
MEBN: A language for first-order Bayesian knowledge bases
Artificial Intelligence
RC_Link: Genetic linkage analysis using Bayesian networks
International Journal of Approximate Reasoning
Pivotal pruning of trade-offs in QPNs
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Hypothesis management in situation-specific network construction
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Lazy propagation in junction trees
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Constructing situation specific belief networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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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 decomposilion, 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.