ECMC 2021 Monemvassitis
From Werner KRAUTH
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| ==Ergodicity in bidimensional sphere systems== | ==Ergodicity in bidimensional sphere systems== | ||
| - | Athina Monemvassitis (PhD student), co-supervised by A. Guillin, M. Michel, S. Monteil | + | '''<u>Athina Monemvassitis</u> A. Guillin, M. Michel, S. Monteil''' |
| - | Laboratoire de Mathématiques Blaise Pascal, Université Clermont Auvergne | + | '''''Laboratoire de Mathématiques Blaise Pascal, Université Clermont Auvergne (France)''''' |
| - | Abstract : Event-chain Monte Carlo (ECMC) algorithms are fast irreversible Markov-chain Monte Carlo methods. Developed first in hard sphere systems [1], they rely on the breaking of the detailed balance condition to speed up the sampling with respect to their reversible counterparts. However, to insure the sampling of the right target distribution they need the property of ergodicity. I will present a proof of ergodicity (resp. connectivity) for bidimensional soft (resp. hard) sphere systems, casting the ECMC methods in the framework of Piecewise Deterministic Markov Processes [2]. | + | '''Abstract''' Event-chain Monte Carlo (ECMC) algorithms are fast irreversible Markov-chain Monte Carlo methods. Developed first in hard sphere systems [1], they rely on the breaking of the detailed balance condition to speed up the sampling with respect to their reversible counterparts. However, to insure the sampling of the right target distribution they need the property of ergodicity. I will present a proof of ergodicity (resp. connectivity) for bidimensional soft (resp. hard) sphere systems, casting the ECMC methods in the framework of Piecewise Deterministic Markov Processes [2]. |
| [1] Bernard, Etienne P. and Krauth, Werner and Wilson, David B. "Event-chain Monte Carlo algorithms for hard-sphere systems." Phys. Rev. E, vol. 80, 2009. | [1] Bernard, Etienne P. and Krauth, Werner and Wilson, David B. "Event-chain Monte Carlo algorithms for hard-sphere systems." Phys. Rev. E, vol. 80, 2009. | ||
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| [2] Davis, M. H. A. “Piecewise-Deterministic Markov Processes: A General Class of Non-Diffusion Stochastic Models.” Journal of the Royal Statistical Society. Series B (Methodological), vol. 46, no. 3, 1984. | [2] Davis, M. H. A. “Piecewise-Deterministic Markov Processes: A General Class of Non-Diffusion Stochastic Models.” Journal of the Royal Statistical Society. Series B (Methodological), vol. 46, no. 3, 1984. | ||
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| '''Slides''' | '''Slides''' | ||
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| '''Further material''' | '''Further material''' | ||
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| [[Workshop_ECMC_11_May_2021|back to 2021 ECMC workshop]] | [[Workshop_ECMC_11_May_2021|back to 2021 ECMC workshop]] | ||
Revision as of 12:04, 10 May 2021
Ergodicity in bidimensional sphere systems
Athina Monemvassitis A. Guillin, M. Michel, S. Monteil
Laboratoire de Mathématiques Blaise Pascal, Université Clermont Auvergne (France)
Abstract Event-chain Monte Carlo (ECMC) algorithms are fast irreversible Markov-chain Monte Carlo methods. Developed first in hard sphere systems [1], they rely on the breaking of the detailed balance condition to speed up the sampling with respect to their reversible counterparts. However, to insure the sampling of the right target distribution they need the property of ergodicity. I will present a proof of ergodicity (resp. connectivity) for bidimensional soft (resp. hard) sphere systems, casting the ECMC methods in the framework of Piecewise Deterministic Markov Processes [2].
[1] Bernard, Etienne P. and Krauth, Werner and Wilson, David B. "Event-chain Monte Carlo algorithms for hard-sphere systems." Phys. Rev. E, vol. 80, 2009.
[2] Davis, M. H. A. “Piecewise-Deterministic Markov Processes: A General Class of Non-Diffusion Stochastic Models.” Journal of the Royal Statistical Society. Series B (Methodological), vol. 46, no. 3, 1984.
Slides
Recording
Further material
