Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic inference and influence diagrams
Operations Research
Probabilistic inference in multiply connected belief networks using loop cutsets
International Journal of Approximate Reasoning
Local expression languages for probabilistic dependence: a preliminary report
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Fusion and propagation with multiple observations in belief networks
Artificial Intelligence
On the Desirability of Acyclic Database Schemes
Journal of the ACM (JACM)
Evidence Absorption and Propagation through Evidence Reversals
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
A combination of cutset conditioning with clique-tree propagation in the Pathfinder system
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Probabilistic similarity networks
Probabilistic similarity networks
A computational model for causal and diagnostic reasoning in inference systems
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Constrained abductive reasoning with fuzzy parameters in Bayesian networks
Computers and Operations Research
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
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In this paper we propose a new approach to probabilistic inference on belief networks, global conditioning, which is a simple generalization of Pearl's (1986b) method of loop-cutset conditioning. We show that global conditioning, as well as loop-cutset conditioning, can be thought of as a special case of the method of Lauritzen and Spiegelhalter (1988) as refined by Jensen et al (1990a; 1990b). Nonetheless, this approach provides new opportunities for parallel processing and, in the case of sequential processing, a tradeoff of time for memory. We also show how a hybrid method (Suermondt and others 1990) combining loop-cutset conditioning with Jensen's method can be viewed within our framework. By exploring the relationships between these methods, we develop a unifying framework in which the advantages of each approach can be combined successfully.