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
Fil: Budde, Carlos Esteban. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Fil: Budde, Carlos Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: D'Argenio, Pedro Ruben. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Fil: D'Argenio, Pedro Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Hermanns, Holger. Universität des Saarlandes. Fakultät für Mathematik und Informatik; Alemania.
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.
publishedVersion
Fil: Budde, Carlos Esteban. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Fil: Budde, Carlos Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: D'Argenio, Pedro Ruben. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Fil: D'Argenio, Pedro Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Hermanns, Holger. Universität des Saarlandes. Fakultät für Mathematik und Informatik; Alemania.
Ciencias de la Computación
Fuente
ISSN: 0302-9743
Materia
Rare event
Goal state
Importance sampling
Importance function
Tandem queue
Nivel de accesibilidad
acceso abierto
Condiciones de uso
Repositorio
Repositorio Digital Universitario (UNC)
Institución
Universidad Nacional de Córdoba
OAI Identificador
oai:rdu.unc.edu.ar:11086/27279
Acceder
id RDUUNC_a00bdbb2b35afb58aa2e003ec0df5476
oai_identifier_str oai:rdu.unc.edu.ar:11086/27279
network_acronym_str RDUUNC
repository_id_str 2572
network_name_str Repositorio Digital Universitario (UNC)
spelling Rare event simulation with fully automated Importance splittingBudde, Carlos EstebanD'Argenio, Pedro RubenHermanns, HolgerRare eventGoal stateImportance samplingImportance functionTandem queueFil: Budde, Carlos Esteban. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Fil: Budde, Carlos Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: D'Argenio, Pedro Ruben. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Fil: D'Argenio, Pedro Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Hermanns, Holger. Universität des Saarlandes. Fakultät für Mathematik und Informatik; Alemania.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.publishedVersionFil: Budde, Carlos Esteban. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Fil: Budde, Carlos Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: D'Argenio, Pedro Ruben. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Fil: D'Argenio, Pedro Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Hermanns, Holger. Universität des Saarlandes. Fakultät für Mathematik und Informatik; Alemania.Ciencias de la Computación2015info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/11086/27279https://doi.org/10.1007/978-3-319-23267-6_18https://doi.org/10.1007/978-3-319-23267-6_18ISSN: 0302-9743reponame:Repositorio Digital Universitario (UNC)instname:Universidad Nacional de Córdobainstacron:UNCenginfo:eu-repo/semantics/openAccess2025-09-29T13:42:33Zoai:rdu.unc.edu.ar:11086/27279Institucionalhttps://rdu.unc.edu.ar/Universidad públicaNo correspondehttp://rdu.unc.edu.ar/oai/snrdoca.unc@gmail.comArgentinaNo correspondeNo correspondeNo correspondeopendoar:25722025-09-29 13:42:34.017Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdobafalse
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
Goal state
Importance sampling
Importance function
Tandem queue
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
Goal state
Importance sampling
Importance function
Tandem queue
topic Rare event
Goal state
Importance sampling
Importance function
Tandem queue
dc.description.none.fl_txt_mv Fil: Budde, Carlos Esteban. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Fil: Budde, Carlos Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: D'Argenio, Pedro Ruben. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Fil: D'Argenio, Pedro Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Hermanns, Holger. Universität des Saarlandes. Fakultät für Mathematik und Informatik; Alemania.
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.
publishedVersion
Fil: Budde, Carlos Esteban. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Fil: Budde, Carlos Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: D'Argenio, Pedro Ruben. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Fil: D'Argenio, Pedro Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
Fil: Hermanns, Holger. Universität des Saarlandes. Fakultät für Mathematik und Informatik; Alemania.
Ciencias de la Computación
description Fil: Budde, Carlos Esteban. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
publishDate 2015
dc.date.none.fl_str_mv 2015
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/11086/27279
https://doi.org/10.1007/978-3-319-23267-6_18
https://doi.org/10.1007/978-3-319-23267-6_18
url http://hdl.handle.net/11086/27279
https://doi.org/10.1007/978-3-319-23267-6_18
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv ISSN: 0302-9743
reponame:Repositorio Digital Universitario (UNC)
instname:Universidad Nacional de Córdoba
instacron:UNC
reponame_str Repositorio Digital Universitario (UNC)
collection Repositorio Digital Universitario (UNC)
instname_str Universidad Nacional de Córdoba
instacron_str UNC
institution UNC
repository.name.fl_str_mv Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdoba
repository.mail.fl_str_mv oca.unc@gmail.com
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score 13.070432

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