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
Structuring conditional relationships in influence diagrams
Operations Research
A Comparison of Graphical Techniques for Asymmetric Decision Problems
Management Science
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Machine Learning
Representing and Solving Asymmetric Bayesian Decision Problems
UAI '00 Proceedings of the 16th Conference on 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
Welldefined decision scenarios
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Efficient value of information computation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
An anytime algorithm for decision making under uncertainty
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
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Modeling challenges with influence diagrams: Constructing probability and utility models
Decision Support Systems
Simulation method for solving hybrid influence diagrams in decision making
Proceedings of the Winter Simulation Conference
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Although influence diagrams are powerful tools for representing and solving complex decision-making problems, their evaluation may require an enormous computational effort and this is a primary issue when processing real-world models. We shall propose an approximate inference algorithm to deal with very large models. For such models, it may be unfeasible to achieve an exact solution. This anytime algorithm returns approximate solutions which are increasingly refined as computation progresses, producing knowledge that offers insight into the decision problem.