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FIG: The Finite Improbability Generator

This paper introduces the statistical model checker FIGV, that estimates transient and steady-state reachability properties in stochastic automata. This software tool specialises in Rare Event Simulation via importance splitting, and implements the algorithms RESTART and Fixed Effort. FIG is push-bu...

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Detalles Bibliográficos
Autor principal: Budde, Carlos E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7439731/
http://dx.doi.org/10.1007/978-3-030-45190-5_27
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author Budde, Carlos E.
author_facet Budde, Carlos E.
author_sort Budde, Carlos E.
collection PubMed
description This paper introduces the statistical model checker FIGV, that estimates transient and steady-state reachability properties in stochastic automata. This software tool specialises in Rare Event Simulation via importance splitting, and implements the algorithms RESTART and Fixed Effort. FIG is push-button automatic since the user need not define an importance function: this function is derived from the model specification plus the property query. The tool operates with Input/Output Stochastic Automata with Urgency, aka IOSA models, described either in the native syntax or in the JANI exchange format. The theory backing FIG has demonstrated good efficiency, comparable to optimal importance splitting implemented ad hoc for specific models. Written in C++, FIG can outperform other state-of-the-art tools for Rare Event Simulation.
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spelling pubmed-74397312020-08-21 FIG: The Finite Improbability Generator Budde, Carlos E. Tools and Algorithms for the Construction and Analysis of Systems Article This paper introduces the statistical model checker FIGV, that estimates transient and steady-state reachability properties in stochastic automata. This software tool specialises in Rare Event Simulation via importance splitting, and implements the algorithms RESTART and Fixed Effort. FIG is push-button automatic since the user need not define an importance function: this function is derived from the model specification plus the property query. The tool operates with Input/Output Stochastic Automata with Urgency, aka IOSA models, described either in the native syntax or in the JANI exchange format. The theory backing FIG has demonstrated good efficiency, comparable to optimal importance splitting implemented ad hoc for specific models. Written in C++, FIG can outperform other state-of-the-art tools for Rare Event Simulation. 2020-03-13 /pmc/articles/PMC7439731/ http://dx.doi.org/10.1007/978-3-030-45190-5_27 Text en © The Author(s) 2020 Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
spellingShingle Article
Budde, Carlos E.
FIG: The Finite Improbability Generator
title FIG: The Finite Improbability Generator
title_full FIG: The Finite Improbability Generator
title_fullStr FIG: The Finite Improbability Generator
title_full_unstemmed FIG: The Finite Improbability Generator
title_short FIG: The Finite Improbability Generator
title_sort fig: the finite improbability generator
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7439731/
http://dx.doi.org/10.1007/978-3-030-45190-5_27
work_keys_str_mv AT buddecarlose figthefiniteimprobabilitygenerator