A Functional Central Limit Theorem for a Class of Interacting Markov Chain Monte Carlo Models

Bernard Bercu 1, 2 Pierre Del Moral 1, 3, * Arnaud Doucet 4, 5
* Auteur correspondant
1 IMB - Institut de Mathématiques de Bordeaux
2 CQFD - Quality control and dynamic reliability
IMB - Institut de Mathématiques de Bordeaux, Inria Bordeaux - Sud-Ouest
3 ALEA - Advanced Learning Evolutionary Algorithms
Inria Bordeaux - Sud-Ouest, UB - Université de Bordeaux, CNRS - Centre National de la Recherche Scientifique : UMR5251
4 Dept of Statistics & Dept of Computer Science
5 Statistics - Department of Statistics
Abstract : We present a functional central limit theorem for a new class of interaction Markov chain Monte Carlo interpretations of discrete generation measure valued equations. We provide an original stochastic analysis based on semigroup techniques on distribution spaces and fluctuation theorems for self-interaction random fields. Besides the fluctuation analysis of these models, we also present a series of sharp $\LL_m$-mean error bounds in terms of the semigroup associated with the first order expansion of the limiting measure valued process, yielding what seems to be the first results of this type for this class of interacting processes. We illustrate these results in the context of Feynman-Kac integration semigroups arising in physics, biology and stochastic engineering science.
Keywords : and Feynman-Kac integrals Multivariate and functional central limit theorems random fields martingale limit theorems self-interacting Markov chains Markov chain Monte Carlo models and Feynman-Kac integrals.
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Article dans une revue
Electronic Journal of Probability, Institute of Mathematical Statistics (IMS), 2009, 14 (73), pp.2130-2155
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Contributeur : Pierre Del Moral <>
Soumis le : jeudi 18 juin 2009 - 08:45:28
Dernière modification le : mercredi 14 décembre 2016 - 01:07:05
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Bernard Bercu, Pierre Del Moral, Arnaud Doucet. A Functional Central Limit Theorem for a Class of Interacting Markov Chain Monte Carlo Models. Electronic Journal of Probability, Institute of Mathematical Statistics (IMS), 2009, 14 (73), pp.2130-2155. <inria-00227534v5>

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