Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Operations Research
Probabilistic inference and influence diagrams
Operations Research
A fusion algorithm for solving Bayesian decision problems
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Evidence Absorption and Propagation through Evidence Reversals
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
HUGIN: a shell for building Bayesian belief universes for expert systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
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The analysis of decision making under uncertainty is closely related to the analysis of probabilistic inference. Indeed, much of the research into efficient methods for probabilistic inference in expert systems has been motivated by the fundamental normative arguments of decision theory. In this paper we show how the developments underlying those efficient methods can be applied immediately to decision problems. In addition to general approaches which need know nothing about the actual probabilistic inference method, we suggest some simple modifications to the clustering family of algorithms in order to efficiently incorporate decision making capabilities.