Algorithms for Approximated Inference with Credal Networks

Autores
da Rocha, Jose Carlos F.; de Campos, Cassio P.; Cozman, Fabio G.
Año de publicación
2003
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
A credal network associates convex sets of probability distributions with graph-based models. Inference with credal networks aims at determining intervals on probability measures. Here we describe how a branch-and-bound based approach can be applied to accomplish approximated inference in polytrees iteratively. Our strategy explores a breadth-first version of branch-and-bound to compute outer approximations for the probability intervals. The basic idea is to refine the outer bounds calculated by the A/R+ algorithm until they are sufficiently precise or time/memory constraints have been exceeded.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
credal network
branch-and-bound based
probability intervals
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/184929

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spelling Algorithms for Approximated Inference with Credal Networksda Rocha, Jose Carlos F.de Campos, Cassio P.Cozman, Fabio G.Ciencias Informáticascredal networkbranch-and-bound basedprobability intervalsA credal network associates convex sets of probability distributions with graph-based models. Inference with credal networks aims at determining intervals on probability measures. Here we describe how a branch-and-bound based approach can be applied to accomplish approximated inference in polytrees iteratively. Our strategy explores a breadth-first version of branch-and-bound to compute outer approximations for the probability intervals. The basic idea is to refine the outer bounds calculated by the A/R+ algorithm until they are sufficiently precise or time/memory constraints have been exceeded.Sociedad Argentina de Informática e Investigación Operativa2003-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/184929enginfo:eu-repo/semantics/altIdentifier/issn/1666-1079info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:50:37Zoai:sedici.unlp.edu.ar:10915/184929Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:50:37.418SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Algorithms for Approximated Inference with Credal Networks
title Algorithms for Approximated Inference with Credal Networks
spellingShingle Algorithms for Approximated Inference with Credal Networks
da Rocha, Jose Carlos F.
Ciencias Informáticas
credal network
branch-and-bound based
probability intervals
title_short Algorithms for Approximated Inference with Credal Networks
title_full Algorithms for Approximated Inference with Credal Networks
title_fullStr Algorithms for Approximated Inference with Credal Networks
title_full_unstemmed Algorithms for Approximated Inference with Credal Networks
title_sort Algorithms for Approximated Inference with Credal Networks
dc.creator.none.fl_str_mv da Rocha, Jose Carlos F.
de Campos, Cassio P.
Cozman, Fabio G.
author da Rocha, Jose Carlos F.
author_facet da Rocha, Jose Carlos F.
de Campos, Cassio P.
Cozman, Fabio G.
author_role author
author2 de Campos, Cassio P.
Cozman, Fabio G.
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
credal network
branch-and-bound based
probability intervals
topic Ciencias Informáticas
credal network
branch-and-bound based
probability intervals
dc.description.none.fl_txt_mv A credal network associates convex sets of probability distributions with graph-based models. Inference with credal networks aims at determining intervals on probability measures. Here we describe how a branch-and-bound based approach can be applied to accomplish approximated inference in polytrees iteratively. Our strategy explores a breadth-first version of branch-and-bound to compute outer approximations for the probability intervals. The basic idea is to refine the outer bounds calculated by the A/R+ algorithm until they are sufficiently precise or time/memory constraints have been exceeded.
Sociedad Argentina de Informática e Investigación Operativa
description A credal network associates convex sets of probability distributions with graph-based models. Inference with credal networks aims at determining intervals on probability measures. Here we describe how a branch-and-bound based approach can be applied to accomplish approximated inference in polytrees iteratively. Our strategy explores a breadth-first version of branch-and-bound to compute outer approximations for the probability intervals. The basic idea is to refine the outer bounds calculated by the A/R+ algorithm until they are sufficiently precise or time/memory constraints have been exceeded.
publishDate 2003
dc.date.none.fl_str_mv 2003-09
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
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status_str publishedVersion
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dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1666-1079
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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