Operations Research
Valuation-based systems for Bayesian decision analysis
Operations Research
Decision making using probabilistic inference methods
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
d-Separation: From Theorems to Algorithms
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Lazy propagation in junction trees
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Probabilistic inference in influence diagrams
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Myopic value of information in influence diagrams
UAI'97 Proceedings of the Thirteenth 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
Decomposition of Influence Diagrams
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - New trends in probabilistic graphical models
Node deletion sequences in influence diagrams using genetic algorithms
Statistics and Computing
Hybrid influence diagrams using mixtures of truncated exponentials
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Learning a decision maker's utility function from (possibly) inconsistent behavior
Artificial Intelligence
A comparison of two approaches for solving unconstrained influence diagrams
International Journal of Approximate Reasoning
Learning a decision maker's utility function from (possibly) inconsistent behavior
Artificial Intelligence
Solving linear-quadratic conditional Gaussian influence diagrams
International Journal of Approximate Reasoning
Sequential influence diagrams: A unified asymmetry framework
International Journal of Approximate Reasoning
A forward-backward Monte Carlo method for solving influence diagrams
International Journal of Approximate Reasoning
Variable elimination for influence diagrams with super value nodes
International Journal of Approximate Reasoning
Modeling challenges with influence diagrams: Constructing probability and utility models
Decision Support Systems
Unconstrained influence diagrams
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Representing and solving asymmetric Bayesian decision problems
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Alert systems for production plants: a methodology based on conflict analysis
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Evaluating asymmetric decision problems with binary constraint trees
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
On the tree structure used by lazy propagation for inference in bayesian networks
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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Solving symmetric Bayesian decision problems is a computationally intensive task to perform regardless of the algorithm used. In this paper we propose a method for improving the efficiency of algorithms for solving Bayesian decision problems. The method is based on the principle of lazy evaluation - a principle recently shown to improve the efficiency of inference in Bayesian networks. The basic idea is to maintain decompositions of potentials and to postpone computations for as long as possible. The efficiency improvements obtained with the lazy evaluation based method is emphasized through examples. Finally, the lazy evaluation based method is compared with the HUGIN and valuation-based systems architectures for solving symmetric Bayesian decision problems.