Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Constraint propagation with imprecise conditional probabilities
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Towards precision of probabilistic bounds propagation
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Importance sampling in Bayesian networks using probability trees
Computational Statistics & Data Analysis
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Probabilistic partial evaluation: exploiting rule structure in probabilistic inference
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Robustness analysis of Bayesian networks with local convex sets of distributions
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Nonuniform dynamic discretization in hybrid networks
UAI'97 Proceedings of the Thirteenth 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
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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The paper shows a method to compute a posteriori intervals of probabilities when the initial conditional information is given also with intervals of probabilities. The right way of doing an exact computation is with the associated convex set of probabilities. Probability trees are used to represent these initial conditional convex sets, because they save enormously the required space. This paper proposes a simulated annealing algorithm, using probability trees as the representation of the convex sets, in order to compute the a posteriori intervals.