Rare event simulation with fully automated importance splitting

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 occur...

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Detalles Bibliográficos
Autores: Budde, Carlos Esteban, D'argenio, Pedro Ruben, Hermanns, Holger
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2015
País:Argentina
Institución:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositorio:CONICET Digital (CONICET)
Idioma:inglés
OAI Identifier:oai:ri.conicet.gov.ar:11336/69334
Acceso en línea:http://hdl.handle.net/11336/69334
Access Level:acceso abierto
Palabra clave:Rare Event Simulation
Importance Splitting
Restart
Statistical Model Checking
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
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network_acronym_str AR
network_name_str Argentina
repository_id_str
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 Técnicas2024-05-08T13:34:07Zoai: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:34982024-05-08 13:34:08.004CONICET 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
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
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
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
topic Rare Event Simulation
Importance Splitting
Restart
Statistical Model Checking
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
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
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
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 15,811543

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