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This paper investigates the concept of strong conditional independence for sets of probability measures. Couso, Moral and Walley [7] have studied different possible definitions for unconditional independence in imprecise probabilities. Two of them were considered as more relevant: epistemic independence and strong independence. In this paper, we show that(More)
This paper proposes two new algorithms for inference in credal networks. These algorithms enable probability intervals to be obtained for the states of a given query variable. The first algorithm is approximate and uses the hill-climbing technique in the Shenoy–Shafer architecture to propagate in join trees; the second is exact and is a modification of(More)
paper presents non-random algorithms for approximate computation in Bayesian networks. They are based on the use of probability trees to represent probability potentials, using the Kullback-Leibler cross entropy as a measure of the error of the approximation. Different alternatives are presented and tested in several experiments with difficult propagation(More)
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(More)
In this paper, we investigate the application of the ideas behind Lazy propagation to the Penniless propagation scheme. Probabilistic potentials attached to the messages and the nodes of the join tree are represented in a factorized way as a product of (approximate) probability trees, and the combination operations are postponed until they are compulsory(More)
This paper reviews algorithms for local computation with imprecise probabilities. These algorithms try to solve problems of inference (calculation of conditional or unconditional probabilities) in cases in which there are a large number of variables. There are two main types depending on the nature of assumed independence relationships in each case. In both(More)