118 research outputs found

    Inference in credal networks: branch-and-bound methods and the A/R+ algorithm

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    AbstractA credal network is a graphical representation for a set of joint probability distributions. In this paper we discuss algorithms for exact and approximate inferences in credal networks. We propose a branch-and-bound framework for inference, and focus on inferences for polytree-shaped networks. We also propose a new algorithm, A/R+, for outer approximations in polytree-shaped credal networks

    Hill-climbing and branch-and-bound algorithms for exact and approximate inference in credal networks

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    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 Rocha and Cozman’s branch-and-bound algorithm, but applied to general directed acyclic graphs.TIN2004-06204-C03-0

    ISIPTA'07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications

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    Pseudo Credal Networks for Inference With Probability Intervals

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    Abstract The computation of the inference corresponds to an NP-hard problem even for a single connected credal network. The novel concept of pseudo networks is proposed as an alternative to reduce the computational cost of probabilistic inference in credal networks and overcome the computational cost of existing methods. The method allows identifying the combination of intervals that optimizes the probability values of each state of the queried variable from the credal network. In the case of no evidence, the exact probability bounds of the query variable are calculated. When new evidence is inserted into the network, the outer and inner approximations of the query variable are computed by means of the marginalization of the joint probability distributions of the pseudo networks. The applicability of the proposed methodology is shown by solving numerical case studies.</jats:p
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