Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Valuation-based systems for Bayesian decision analysis
Operations Research
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Evaluating Influence Diagrams using LIMIDs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
A review of explanation methods for Bayesian networks
The Knowledge Engineering Review
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
Use of Elvira's explanation facility for debugging probabilistic expert systems
Knowledge-Based Systems
A comparison of two approaches for solving unconstrained influence diagrams
International Journal of Approximate Reasoning
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Journal of Artificial Intelligence Research
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
Multiplicative factorization of noisy-max
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Structured arc reversal and simulation of dynamic probabilistic networks
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
From influence diagrams to junction trees
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Variations over the message computation algorithm of lazy propagation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Explanation of Bayesian Networks and Influence Diagrams in Elvira
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Sensitivity analysis in influence diagrams
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In the original formulation of influence diagrams (IDs), each model contained exactly one utility node. In 1990, Tatman and Shachtar introduced the possibility of having super value nodes that represent a combination of their parents' utility functions. They also proposed an arc-reversal algorithm for IDs with super value nodes. In this paper we propose a variable-elimination algorithm for influence diagrams with super value nodes which is faster in most cases, requires less memory in general, introduces much fewer redundant (i.e., unnecessary) variables in the resulting policies, may simplify sensitivity analysis, and can speed up inference in IDs containing canonical models, such as the noisy OR.