Rare event simulation with fully automated importance splitting

Autores
Budde, Carlos Esteban; D'argenio, Pedro Ruben; Hermanns, Holger
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Probabilistic model checking is a powerful tool for analysing probabilistic systems but it can only be efficiently applied to Markov models. Monte Carlo simulation provides an alternative for the generality of stochastic processes, but becomes infeasible if the value to estimate depends on the occurrence of rare events. To combat this problem, intelligent simulation strategies exist to lower the estimation variance and hence reduce the simulation time. Importance splitting is one such technique, but requires a guiding function typically defined in an ad hoc fashion by an expert in the field. We present an automatic derivation of the importance function from the model description. A prototypical tool was developed and tested on several Markov models, compared to analytically and numerically calculated results and to results of typical ad hoc importance functions, showing the feasibility and efficiency of this approach. The technique is easily adapted to general models like GSMPs.
Fil: Budde, Carlos Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
Fil: D'argenio, Pedro Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
Fil: Hermanns, Holger. Universitat Saarland; Alemania
Materia
Rare Event Simulation
Importance Splitting
Restart
Statistical Model Checking
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:11336/69334
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/69334
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Rare event simulation with fully automated importance splittingBudde, Carlos EstebanD'argenio, Pedro RubenHermanns, HolgerRare Event SimulationImportance SplittingRestartStatistical Model Checkinghttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Probabilistic model checking is a powerful tool for analysing probabilistic systems but it can only be efficiently applied to Markov models. Monte Carlo simulation provides an alternative for the generality of stochastic processes, but becomes infeasible if the value to estimate depends on the occurrence of rare events. To combat this problem, intelligent simulation strategies exist to lower the estimation variance and hence reduce the simulation time. Importance splitting is one such technique, but requires a guiding function typically defined in an ad hoc fashion by an expert in the field. We present an automatic derivation of the importance function from the model description. A prototypical tool was developed and tested on several Markov models, compared to analytically and numerically calculated results and to results of typical ad hoc importance functions, showing the feasibility and efficiency of this approach. The technique is easily adapted to general models like GSMPs.Fil: Budde, Carlos Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: D'argenio, Pedro Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Hermanns, Holger. Universitat Saarland; AlemaniaSpringer2015-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/69334Budde, Carlos Esteban; D'argenio, Pedro Ruben; Hermanns, Holger; Rare event simulation with fully automated importance splitting; Springer; Lecture Notes in Computer Science; 9272; 8-2015; 275-2900302-9743CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-23267-6_18info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/chapter/10.1007/978-3-319-23267-6_18info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:34:57Zoai:ri.conicet.gov.ar:11336/69334instacron: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 09:34:57.329CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Rare event simulation with fully automated importance splitting
title Rare event simulation with fully automated importance splitting
spellingShingle Rare event simulation with fully automated importance splitting
Budde, Carlos Esteban
Rare Event Simulation
Importance Splitting
Restart
Statistical Model Checking
title_short Rare event simulation with fully automated importance splitting
title_full Rare event simulation with fully automated importance splitting
title_fullStr Rare event simulation with fully automated importance splitting
title_full_unstemmed Rare event simulation with fully automated importance splitting
title_sort Rare event simulation with fully automated importance splitting
dc.creator.none.fl_str_mv Budde, Carlos Esteban
D'argenio, Pedro Ruben
Hermanns, Holger
author Budde, Carlos Esteban
author_facet Budde, Carlos Esteban
D'argenio, Pedro Ruben
Hermanns, Holger
author_role author
author2 D'argenio, Pedro Ruben
Hermanns, Holger
author2_role author
author
dc.subject.none.fl_str_mv Rare Event Simulation
Importance Splitting
Restart
Statistical Model Checking
topic Rare Event Simulation
Importance Splitting
Restart
Statistical Model Checking
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Probabilistic model checking is a powerful tool for analysing probabilistic systems but it can only be efficiently applied to Markov models. Monte Carlo simulation provides an alternative for the generality of stochastic processes, but becomes infeasible if the value to estimate depends on the occurrence of rare events. To combat this problem, intelligent simulation strategies exist to lower the estimation variance and hence reduce the simulation time. Importance splitting is one such technique, but requires a guiding function typically defined in an ad hoc fashion by an expert in the field. We present an automatic derivation of the importance function from the model description. A prototypical tool was developed and tested on several Markov models, compared to analytically and numerically calculated results and to results of typical ad hoc importance functions, showing the feasibility and efficiency of this approach. The technique is easily adapted to general models like GSMPs.
Fil: Budde, Carlos Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
Fil: D'argenio, Pedro Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
Fil: Hermanns, Holger. Universitat Saarland; Alemania
description Probabilistic model checking is a powerful tool for analysing probabilistic systems but it can only be efficiently applied to Markov models. Monte Carlo simulation provides an alternative for the generality of stochastic processes, but becomes infeasible if the value to estimate depends on the occurrence of rare events. To combat this problem, intelligent simulation strategies exist to lower the estimation variance and hence reduce the simulation time. Importance splitting is one such technique, but requires a guiding function typically defined in an ad hoc fashion by an expert in the field. We present an automatic derivation of the importance function from the model description. A prototypical tool was developed and tested on several Markov models, compared to analytically and numerically calculated results and to results of typical ad hoc importance functions, showing the feasibility and efficiency of this approach. The technique is easily adapted to general models like GSMPs.
publishDate 2015
dc.date.none.fl_str_mv 2015-08
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/11336/69334
Budde, Carlos Esteban; D'argenio, Pedro Ruben; Hermanns, Holger; Rare event simulation with fully automated importance splitting; Springer; Lecture Notes in Computer Science; 9272; 8-2015; 275-290
0302-9743
CONICET Digital
CONICET
url http://hdl.handle.net/11336/69334
identifier_str_mv Budde, Carlos Esteban; D'argenio, Pedro Ruben; Hermanns, Holger; Rare event simulation with fully automated importance splitting; Springer; Lecture Notes in Computer Science; 9272; 8-2015; 275-290
0302-9743
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-23267-6_18
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/chapter/10.1007/978-3-319-23267-6_18
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 Springer
publisher.none.fl_str_mv Springer
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
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score 13.070432

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