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Mean field simulation for Monte Carlo integration

Pierre del Moral 1, 2 
1 ALEA - Advanced Learning Evolutionary Algorithms
Inria Bordeaux - Sud-Ouest, UB - Université de Bordeaux, CNRS - Centre National de la Recherche Scientifique : UMR5251
Abstract : In the last three decades, there has been a dramatic increase in the use of interacting particle methods as a powerful tool in real-world applications of Monte Carlo simulation in computational physics, population biology, computer sciences, and statistical machine learning. Ideally suited to parallel and distributed computation, these advanced particle algorithms include nonlinear interacting jump diffusions; quantum, diffusion, and resampled Monte Carlo methods; Feynman-Kac particle models; genetic and evolutionary algorithms; sequential Monte Carlo methods; adaptive and interacting Markov chain Monte Carlo models; bootstrapping methods; ensemble Kalman filters; and interacting particle filters.
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Contributor : Pierre Del Moral Connect in order to contact the contributor
Submitted on : Thursday, January 16, 2014 - 3:00:14 PM
Last modification on : Friday, February 4, 2022 - 3:22:05 AM


  • HAL Id : hal-00932211, version 1



Pierre del Moral. Mean field simulation for Monte Carlo integration. Chapman&Hall, pp.626, 2013, Monographs on Statistics & Applied Probability. ⟨hal-00932211⟩



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