Approximate algorithms for credal networks with binary variables

Jaime Shinsuke Ide, Fabio Gagliardi Cozman

Resultado de la investigación: Contribución a una revistaArtículo

12 Citas (Scopus)

Resumen

This paper presents a family of algorithms for approximate inference in credal networks (that is, models based on directed acyclic graphs and set-valued probabilities) that contain only binary variables. Such networks can represent incomplete or vague beliefs, lack of data, and disagreements among experts; they can also encode models based on belief functions and possibilistic measures. All algorithms for approximate inference in this paper rely on exact inferences in credal networks based on polytrees with binary variables, as these inferences have polynomial complexity. We are inspired by approximate algorithms for Bayesian networks; thus the Loopy 2U algorithm resembles Loopy Belief Propagation, while the Iterated Partial Evaluation and Structured Variational 2U algorithms are, respectively, based on Localized Partial Evaluation and variational techniques. © 2007 Elsevier Inc. All rights reserved.
Idioma originalInglés estadounidense
Páginas (desde-hasta)275-296
Número de páginas22
PublicaciónInternational Journal of Approximate Reasoning
DOI
EstadoPublicada - 1 abr 2008
Publicado de forma externa

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