Decision-theoretic troubleshooting
Communications of the ACM
Machine Learning - Special issue on learning with probabilistic representations
An Approximate Nonmyopic Computation for Value of Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sensitive Analysis for Threshold Decision Making with Bayesian Belief Networks
AI*IA '99 Proceedings of the 6th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Efficient non-myopic value-of-information computation for influence diagrams
International Journal of Approximate Reasoning
Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks
Complexity results and approximation strategies for MAP explanations
Journal of Artificial Intelligence Research
Optimal value of information in graphical models
Journal of Artificial Intelligence Research
On stopping evidence gathering for diagnostic Bayesian networks
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Journal of Artificial Intelligence Research
Myopic value of information in influence diagrams
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Reasoning about bayesian network classifiers
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Same-decision probability: A confidence measure for threshold-based decisions
International Journal of Approximate Reasoning
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When using graphical models for decision making, the presence of unobserved variables may hinder our ability to reach the correct decision. A fundamental question here is whether or not one is ready to make a decision (stopping criteria), and if not, what additional observations should be made in order to better prepare for a decision (selection criteria). A recently introduced notion, the Same-Decision Probability (SDP), has been shown to be useful as both a stopping and a selection criteria. This query has been shown to be highly intractable, being PPPP-complete, and is exemplary of a class of queries which correspond to the computation of certain expectations. We propose the first exact algorithm for computing the SDP in this paper, and demonstrate its effectiveness on several real and synthetic networks. We also present a new complexity result for computing the SDP on models with a Naive Bayes structure.