Convex combination belief propagation

Anna Grim and Pedro Felzenszwalb

Applied Mathematics and Computation, 2023, vol. 438, issue C

Abstract: We present new message passing algorithms for performing inference with graphical models. Our methods are designed for the most difficult inference problems where loopy belief propagation and other heuristics fail to converge. Belief propagation is guaranteed to converge when the underlying graphical model is acyclic, but can fail to converge and is sensitive to initialization when the underlying graph has complex topology. This paper describes modifications to the standard belief propagation algorithms that lead to methods that converge to unique solutions on graphical models with arbitrary topology and potential functions.

Keywords: Belief propagation; Statistical inference; Graphical model; Image processing; Artificial intelligence (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:438:y:2023:i:c:s0096300322006464

DOI: 10.1016/j.amc.2022.127572

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