Operations Research
Management Science
Valuation-based systems for Bayesian decision analysis
Operations Research
LAZY propagation: a junction tree inference algorithm based on lazy evaluation
Artificial Intelligence
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Stable local computation with conditional Gaussian distributions
Statistics and Computing
Exact Inference in Networks with Discrete Children of Continuous Parents
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Solving Influence Diagrams using HUGIN, Shafer-Shenoy and Lazy Propagation
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Representing and Solving Decision Problems with Limited Information
Management Science
Decision Analysis
Lazy evaluation of symmetric Bayesian decision problems
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Efficiency of influence diagram models with continuous decision variables
Decision Support Systems
Simulation method for solving hybrid influence diagrams in decision making
Proceedings of the Winter Simulation Conference
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This paper considers the problem of solving Bayesian decision problems with a mixture of continuous and discrete variables. We focus on exact evaluation of linear-quadratic conditional Gaussian influence diagrams (LQCG influence diagrams) with additively decomposing utility functions. Based on new and existing representations of probability and utility potentials, we derive a method for solving LQCG influence diagrams based on variable elimination. We show how the computations performed during evaluation of a LQCG influence diagram can be organized in message passing schemes based on Shenoy-Shafer and Lazy propagation. The proposed architectures are the first architectures for efficient exact solution of LQCG influence diagrams exploiting an additively decomposing utility function.