Operations Research
Management Science
Valuation-based systems for Bayesian decision analysis
Operations Research
Solving Influence Diagrams using HUGIN, Shafer-Shenoy and Lazy Propagation
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Representing and Solving Decision Problems with Limited Information
Management Science
Lazy evaluation of symmetric Bayesian decision problems
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Penniless propagation with mixtures of truncated exponentials
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for representing continuous chance variables in influence diagrams. Also, MTE potentials can be used to approximate utility functions. This paper introduces MTE influence diagrams, which can represent decision problems without restrictions on the relationships between continuous and discrete chance variables, without limitations on the distributions of continuous chance variables, and without limitations on the nature of the utility functions. In MTE influence diagrams, all probability distributions and the joint utility function (or its multiplicative factors) are represented by MTE potentials and decision nodes are assumed to have discrete state spaces. MTE influence diagrams are solved by variable elimination using a fusion algorithm.