Bayesian network semantics for Petri nets

Autores
Bruni, Roberto; Melgratti, Hernan Claudio; Montanari, Ugo
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Recent work by the authors equips Petri occurrence nets (PN) with probability distributions which fully replace nondeterminism. To avoid the so-called confusion problem, the construction imposes additional causal dependencies which restrict choices within certain subnets called structural branching cells (s-cells). Bayesian nets (BN) are usually structured as partial orders where nodes define conditional probability distributions. In the paper, we unify the two structures in terms of Symmetric Monoidal Categories (SMC), so that we can apply to PN ordinary analysis techniques developed for BN. Interestingly, it turns out that PN which cannot be SMC-decomposed are exactly s-cells. This result confirms the importance for Petri nets of both SMC and s-cells.
Fil: Bruni, Roberto. Università degli Studi di Pisa; Italia
Fil: Melgratti, Hernan Claudio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
Fil: Montanari, Ugo. Università degli Studi di Pisa; Italia
Materia
BAYESIAN NETS
BRANCHING CELLS
CONDITIONAL PROBABILITY DISTRIBUTIONS
CONFUSION
FORWARD AND BACKWARD INFERENCE
KLEISLI CATEGORIES
PETRI NETS
SYMMETRIC MONOIDAL CATEGORIES
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/123313
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/123313
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Bayesian network semantics for Petri netsBruni, RobertoMelgratti, Hernan ClaudioMontanari, UgoBAYESIAN NETSBRANCHING CELLSCONDITIONAL PROBABILITY DISTRIBUTIONSCONFUSIONFORWARD AND BACKWARD INFERENCEKLEISLI CATEGORIESPETRI NETSSYMMETRIC MONOIDAL CATEGORIEShttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Recent work by the authors equips Petri occurrence nets (PN) with probability distributions which fully replace nondeterminism. To avoid the so-called confusion problem, the construction imposes additional causal dependencies which restrict choices within certain subnets called structural branching cells (s-cells). Bayesian nets (BN) are usually structured as partial orders where nodes define conditional probability distributions. In the paper, we unify the two structures in terms of Symmetric Monoidal Categories (SMC), so that we can apply to PN ordinary analysis techniques developed for BN. Interestingly, it turns out that PN which cannot be SMC-decomposed are exactly s-cells. This result confirms the importance for Petri nets of both SMC and s-cells.Fil: Bruni, Roberto. Università degli Studi di Pisa; ItaliaFil: Melgratti, Hernan Claudio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Montanari, Ugo. Università degli Studi di Pisa; ItaliaElsevier Science2020-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/123313Bruni, Roberto; Melgratti, Hernan Claudio; Montanari, Ugo; Bayesian network semantics for Petri nets; Elsevier Science; Theoretical Computer Science; 807; 2-2020; 95-1130304-3975CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.tcs.2019.07.034info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:42:39Zoai:ri.conicet.gov.ar:11336/123313instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:42:39.788CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Bayesian network semantics for Petri nets
title Bayesian network semantics for Petri nets
spellingShingle Bayesian network semantics for Petri nets
Bruni, Roberto
BAYESIAN NETS
BRANCHING CELLS
CONDITIONAL PROBABILITY DISTRIBUTIONS
CONFUSION
FORWARD AND BACKWARD INFERENCE
KLEISLI CATEGORIES
PETRI NETS
SYMMETRIC MONOIDAL CATEGORIES
title_short Bayesian network semantics for Petri nets
title_full Bayesian network semantics for Petri nets
title_fullStr Bayesian network semantics for Petri nets
title_full_unstemmed Bayesian network semantics for Petri nets
title_sort Bayesian network semantics for Petri nets
dc.creator.none.fl_str_mv Bruni, Roberto
Melgratti, Hernan Claudio
Montanari, Ugo
author Bruni, Roberto
author_facet Bruni, Roberto
Melgratti, Hernan Claudio
Montanari, Ugo
author_role author
author2 Melgratti, Hernan Claudio
Montanari, Ugo
author2_role author
author
dc.subject.none.fl_str_mv BAYESIAN NETS
BRANCHING CELLS
CONDITIONAL PROBABILITY DISTRIBUTIONS
CONFUSION
FORWARD AND BACKWARD INFERENCE
KLEISLI CATEGORIES
PETRI NETS
SYMMETRIC MONOIDAL CATEGORIES
topic BAYESIAN NETS
BRANCHING CELLS
CONDITIONAL PROBABILITY DISTRIBUTIONS
CONFUSION
FORWARD AND BACKWARD INFERENCE
KLEISLI CATEGORIES
PETRI NETS
SYMMETRIC MONOIDAL CATEGORIES
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Recent work by the authors equips Petri occurrence nets (PN) with probability distributions which fully replace nondeterminism. To avoid the so-called confusion problem, the construction imposes additional causal dependencies which restrict choices within certain subnets called structural branching cells (s-cells). Bayesian nets (BN) are usually structured as partial orders where nodes define conditional probability distributions. In the paper, we unify the two structures in terms of Symmetric Monoidal Categories (SMC), so that we can apply to PN ordinary analysis techniques developed for BN. Interestingly, it turns out that PN which cannot be SMC-decomposed are exactly s-cells. This result confirms the importance for Petri nets of both SMC and s-cells.
Fil: Bruni, Roberto. Università degli Studi di Pisa; Italia
Fil: Melgratti, Hernan Claudio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
Fil: Montanari, Ugo. Università degli Studi di Pisa; Italia
description Recent work by the authors equips Petri occurrence nets (PN) with probability distributions which fully replace nondeterminism. To avoid the so-called confusion problem, the construction imposes additional causal dependencies which restrict choices within certain subnets called structural branching cells (s-cells). Bayesian nets (BN) are usually structured as partial orders where nodes define conditional probability distributions. In the paper, we unify the two structures in terms of Symmetric Monoidal Categories (SMC), so that we can apply to PN ordinary analysis techniques developed for BN. Interestingly, it turns out that PN which cannot be SMC-decomposed are exactly s-cells. This result confirms the importance for Petri nets of both SMC and s-cells.
publishDate 2020
dc.date.none.fl_str_mv 2020-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/123313
Bruni, Roberto; Melgratti, Hernan Claudio; Montanari, Ugo; Bayesian network semantics for Petri nets; Elsevier Science; Theoretical Computer Science; 807; 2-2020; 95-113
0304-3975
CONICET Digital
CONICET
url http://hdl.handle.net/11336/123313
identifier_str_mv Bruni, Roberto; Melgratti, Hernan Claudio; Montanari, Ugo; Bayesian network semantics for Petri nets; Elsevier Science; Theoretical Computer Science; 807; 2-2020; 95-113
0304-3975
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.tcs.2019.07.034
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844614459684814848
score 13.070432

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