IEEE Transactions on Systems, Man and Cybernetics
Bayesian and non-Bayesian evidential updating
Artificial Intelligence
Operations Research
Higher order probability and intervals
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Probabilistic inference and influence diagrams
Operations Research
Decision analysis with indeterminate or incoherent probabilities
Annals of Operations Research
Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Computing probability intervals under independency constraints
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Decision making with interval influence diagrams
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Robust Learning with Missing Data
Machine Learning
Geometric foundations for interval-based probabilities
Annals of Mathematics and Artificial Intelligence
Strong Conditional Independence for Credal Sets
Annals of Mathematics and Artificial Intelligence
Partially observable Markov decision processes with imprecise parameters
Artificial Intelligence
Modeling challenges with influence diagrams: Constructing probability and utility models
Decision Support Systems
Sequential decision making with partially ordered preferences
Artificial Intelligence
Independence with lower and upper probabilities
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Propagation of 2-monotone lower probabilities on an undirected graph
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Reversibility and equivalence in directed markov fields
Mathematical and Computer Modelling: An International Journal
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A mechanism for performing probabilistic reasoning in influence diagrams using interval rather than point-valued probabilities is described. Procedures for operations corresponding to conditional expectation and Bayesian conditioning in influence diagrams are derived where lower bounds on probabilities are stored at each node. The resulting bounds for the transformed diagram are shown to be the tightest possible within the class of constraints on probability distributions that can be expressed exclusively as lower bounds on the component probabilities of the diagram. Sequences of these operations can be performed to answer probabilistic queries with indeterminacies in the input and for performing sensitivity analysis on an influence diagram. The storage requirements and computational complexity of this approach are comparable to those for point-valued probabilistic inference mechanisms.