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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Autores principales: Budde, Carlos Esteban, D'Argenio, Pedro Ruben, Hermanns, Holger
Formato: article
Lenguaje:Inglés
Publicado: 2022
Materias:
Rare event
Goal state
Importance sampling
Importance function
Tandem queue
Acceso en línea: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
Aporte de:
Repositorio Digital Universitario (UNC) de Universidad Nacional de Córdoba
id I10-R141-11086-27279
record_format dspace
institution Universidad Nacional de Córdoba
institution_str I-10
repository_str R-141
collection Repositorio Digital Universitario (UNC)
language Inglés
topic Rare event
Goal state
Importance sampling
Importance function
Tandem queue
spellingShingle Rare event
Goal state
Importance sampling
Importance function
Tandem queue
Budde, Carlos Esteban
D'Argenio, Pedro Ruben
Hermanns, Holger
Rare event simulation with fully automated Importance splitting
topic_facet Rare event
Goal state
Importance sampling
Importance function
Tandem queue
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.
format article
author Budde, Carlos Esteban
D'Argenio, Pedro Ruben
Hermanns, Holger
author_facet Budde, Carlos Esteban
D'Argenio, Pedro Ruben
Hermanns, Holger
author_sort Budde, Carlos Esteban
title Rare event simulation with fully automated Importance splitting
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
publishDate 2022
url 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
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bdutipo_str Repositorios
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