Evolutionary Markov chain Monte Carlo

M.M. Drugan; D. Thierens

doi:10.1007/978-3-540-24621-3_6

Evolutionary Markov chain Monte Carlo

M.M. Drugan, D. Thierens

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Hoofdstuk › Academic › peer review

14 Citaten (Scopus)

Samenvatting

Markov chain Monte Carlo (MCMC) is a popular class of algorithms to sample from a complex distribution. A key issue in the design of MCMC algorithms is to improve the proposal mechanism and the mixing behaviour. This has led some authors to propose the use of a population of MCMC chains, while others go even further by integrating techniques from evolutionary computation (EC) into the MCMC framework. This merging of MCMC and EC leads to a class of algorithms, we call Evolutionary Markov Chain Monte Carlo (EMCMC). In this paper we first survey existing EMCMC algorithms and categorise them in two classes: family-competitive EMCMC and population-driven EMCMC. Next, we introduce, the Elitist Coupled Acceptance rule and the Fitness Ordered Tempering algorithm.

Originele taal-2	Engels
Titel	Artificial Evolution
Subtitel	6th International Conference, Evolution Artificielle, EA 2003, Marseilles, France, October 27-30, 2003, Revised Selected Papers
Redacteuren	P. Liardet, P. Collet, C. Fonlupt, E. Lutton, M. Schoenauer
Plaats van productie	Berlin
Uitgeverij	Springer
Pagina's	63-76
Aantal pagina's	14
ISBN van geprinte versie	978-3-540-21523-3
DOI's	https://doi.org/10.1007/978-3-540-24621-3_6
Status	Gepubliceerd - 2004
Extern gepubliceerd	Ja

Publicatie series

Naam	LNCS
Volume	2936

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10.1007/978-3-540-24621-3_6

http://www.mendeley.com/research/evolutionary-markov-chain-monte-carlo

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Drugan, M. M., & Thierens, D. (2004). Evolutionary Markov chain Monte Carlo. In P. Liardet, P. Collet, C. Fonlupt, E. Lutton, & M. Schoenauer (editors), Artificial Evolution: 6th International Conference, Evolution Artificielle, EA 2003, Marseilles, France, October 27-30, 2003, Revised Selected Papers (blz. 63-76). (LNCS; Vol. 2936). Springer. https://doi.org/10.1007/978-3-540-24621-3_6

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abstract = "Markov chain Monte Carlo (MCMC) is a popular class of algorithms to sample from a complex distribution. A key issue in the design of MCMC algorithms is to improve the proposal mechanism and the mixing behaviour. This has led some authors to propose the use of a population of MCMC chains, while others go even further by integrating techniques from evolutionary computation (EC) into the MCMC framework. This merging of MCMC and EC leads to a class of algorithms, we call Evolutionary Markov Chain Monte Carlo (EMCMC). In this paper we first survey existing EMCMC algorithms and categorise them in two classes: family-competitive EMCMC and population-driven EMCMC. Next, we introduce, the Elitist Coupled Acceptance rule and the Fitness Ordered Tempering algorithm.",

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Drugan, MM & Thierens, D 2004, Evolutionary Markov chain Monte Carlo. in P Liardet, P Collet, C Fonlupt, E Lutton & M Schoenauer (redactie), Artificial Evolution: 6th International Conference, Evolution Artificielle, EA 2003, Marseilles, France, October 27-30, 2003, Revised Selected Papers. LNCS, vol. 2936, Springer, Berlin, blz. 63-76. https://doi.org/10.1007/978-3-540-24621-3_6

Evolutionary Markov chain Monte Carlo. / Drugan, M.M.; Thierens, D.
Artificial Evolution: 6th International Conference, Evolution Artificielle, EA 2003, Marseilles, France, October 27-30, 2003, Revised Selected Papers. redactie / P. Liardet; P. Collet; C. Fonlupt; E. Lutton; M. Schoenauer. Berlin: Springer, 2004. blz. 63-76 (LNCS; Vol. 2936).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Hoofdstuk › Academic › peer review

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AU - Thierens, D.

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AB - Markov chain Monte Carlo (MCMC) is a popular class of algorithms to sample from a complex distribution. A key issue in the design of MCMC algorithms is to improve the proposal mechanism and the mixing behaviour. This has led some authors to propose the use of a population of MCMC chains, while others go even further by integrating techniques from evolutionary computation (EC) into the MCMC framework. This merging of MCMC and EC leads to a class of algorithms, we call Evolutionary Markov Chain Monte Carlo (EMCMC). In this paper we first survey existing EMCMC algorithms and categorise them in two classes: family-competitive EMCMC and population-driven EMCMC. Next, we introduce, the Elitist Coupled Acceptance rule and the Fitness Ordered Tempering algorithm.

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Evolutionary Markov chain Monte Carlo

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