Operations Research
Probabilistic inference and influence diagrams
Operations Research
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Computing probabilities for probabilistic influence diagrams
Computing probabilities for probabilistic influence diagrams
Valuation-based systems for Bayesian decision analysis
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UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
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UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Directed reduction algorithms and decomposable graphs
UAI '90 Proceedings of the Sixth 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
Efficient non-myopic value-of-information computation for influence diagrams
International Journal of Approximate Reasoning
Modeling challenges with influence diagrams: Constructing probability and utility models
Decision Support Systems
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The potential influence diagram is a generalization of the standard "conditional" influence diagram, a directed network representation for probabilistic inference and decision analysis [Ndilikilikesha, 1991]. It allows efficient inference calculations corresponding exactly to those on undirected graphs. In this paper, we explore the relationship between potential and conditional influence diagrams and provide insight into the properties of the potential influence diagram. In particular, we show how to convert a potential influence diagram into a conditional influence diagram, and how to view the potential influence diagram operations in terms of the conditional influence diagram.