Inference in credal networks: branch-and-bound methods and the A/R+ algorithm
نویسندگان
چکیده
منابع مشابه
Inference in Credal Networks with Branch-and-Bound Algorithms
A credal network associates sets of probability distributions with directed acyclic graphs. Under strong independence assumptions, inference with credal networks is equivalent to a signomial program under linear constraints, a problem that is NP-hard even for categorical variables and polytree models. We describe an approach for inference with polytrees that is based on branch-and-bound optimiz...
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Graph-theoretical representations for sets of probability measures (credal networks) generally display high complexity, and approximate inference seems to be a natural solution for large networks. This paper introduces a variational approach to approximate inference in credal networks: we show how to formulate mean field approximations using naive (fully factorized) and structured (tree-like) s...
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Abstract. Graphical models that represent uncertainty through sets of probability measures are often referred to as credal networks. Polynomial-time exact inference methods are available only for polytree-structured binary credal networks. In this work, we approximate potentially intractable inferences in multiconnected binary networks by tractable inferences in polytree-structures. We propose ...
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Branch and bound algorithms are methods for global optimization in nonconvex problems [LW66, Moo91]. They are nonheuristic, in the sense that they maintain a provable upper and lower bound on the (globally) optimal objective value; they terminate with a certificate proving that the suboptimal point found is ǫ-suboptimal. Branch and bound algorithms can be (and often are) slow, however. In the w...
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ژورنال
عنوان ژورنال: International Journal of Approximate Reasoning
سال: 2005
ISSN: 0888-613X
DOI: 10.1016/j.ijar.2004.10.009