IPE and L2U: approximate algorithms for credal networks

This paper presents two approximate algorithms for inference in graphical models for binary random variables and imprecise probability. Exact inference in such models is extremely challenging in multiply-connected graphs. We describe and implement two new approximate algorithms. The first one is the Iterated Partial Evaluation (IPE) algorithm, directly based on the Localized Partial Evaluation (LPE) technique. The second one is the Loopy 2U (L2U) algorithm, an extension of the popular loopy belief propagation algorithm employed in Bayesian network inference. Experiments show excellent performance for these algorithms.

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