Difference between revisions of "Publications/herault.06.qest"
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{{Publication |
{{Publication |
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| date = 2006-01-01 |
| date = 2006-01-01 |
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| authors = Thomas Hérault, Richard Lassaigne, Sylvain Peyronnet |
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| booktitle = Proceedings of Qest 2006 |
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| abstract = In this paper, we give a brief overview of APMC (Approximate Probabilistic Model Checker). APMC is a model checker that implements approximate probabilistic verification of probabilistic systems. It is based on Monte-Carlo method and the theory of randomized approximation schemes and allows to verify extremely large models without explicitly representing the global transition system. To avoid the state-space explosion phenomenon, APMC gives an accurate approximation of the satisfaction probability of the property instead of the exact value, but using only a very small amount of memory. The version of APMC we present in this paper can now handle efficiently both discrete and continuous time probabilistic systems. |
| abstract = In this paper, we give a brief overview of APMC (Approximate Probabilistic Model Checker). APMC is a model checker that implements approximate probabilistic verification of probabilistic systems. It is based on Monte-Carlo method and the theory of randomized approximation schemes and allows to verify extremely large models without explicitly representing the global transition system. To avoid the state-space explosion phenomenon, APMC gives an accurate approximation of the satisfaction probability of the property instead of the exact value, but using only a very small amount of memory. The version of APMC we present in this paper can now handle efficiently both discrete and continuous time probabilistic systems. |
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year = 2006, |
year = 2006, |
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pages = <nowiki>{</nowiki>129--130<nowiki>}</nowiki>, |
pages = <nowiki>{</nowiki>129--130<nowiki>}</nowiki>, |
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| − | project = <nowiki>{</nowiki>APMC<nowiki>}</nowiki>, |
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abstract = <nowiki>{</nowiki>In this paper, we give a brief overview of APMC |
abstract = <nowiki>{</nowiki>In this paper, we give a brief overview of APMC |
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(Approximate Probabilistic Model Checker). APMC is a model |
(Approximate Probabilistic Model Checker). APMC is a model |
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Latest revision as of 18:57, 4 January 2018
- Authors
- Thomas Hérault, Richard Lassaigne, Sylvain Peyronnet
- Where
- Proceedings of Qest 2006
- Type
- inproceedings
- Projects
- APMC
- Date
- 2006-01-01
Abstract
In this paper, we give a brief overview of APMC (Approximate Probabilistic Model Checker). APMC is a model checker that implements approximate probabilistic verification of probabilistic systems. It is based on Monte-Carlo method and the theory of randomized approximation schemes and allows to verify extremely large models without explicitly representing the global transition system. To avoid the state-space explosion phenomenon, APMC gives an accurate approximation of the satisfaction probability of the property instead of the exact value, but using only a very small amount of memory. The version of APMC we present in this paper can now handle efficiently both discrete and continuous time probabilistic systems.
Bibtex (lrde.bib)
@InProceedings{ herault.06.qest,
author = {Thomas H\'erault and Richard Lassaigne and Sylvain
Peyronnet},
title = {{APMC 3.0}: Approximate verification of Discrete and
Continuous Time Markov Chains},
booktitle = {Proceedings of Qest 2006},
year = 2006,
pages = {129--130},
abstract = {In this paper, we give a brief overview of APMC
(Approximate Probabilistic Model Checker). APMC is a model
checker that implements approximate probabilistic
verification of probabilistic systems. It is based on
Monte-Carlo method and the theory of randomized
approximation schemes and allows to verify extremely large
models without explicitly representing the global
transition system. To avoid the state-space explosion
phenomenon, APMC gives an accurate approximation of the
satisfaction probability of the property instead of the
exact value, but using only a very small amount of memory.
The version of APMC we present in this paper can now handle
efficiently both discrete and continuous time probabilistic
systems.}
}