Population Monte Carlo
Cappé, Olivier; Guillin, Arnaud; Marin, Jean-Michel; Robert, Christian P. (2004), Population Monte Carlo, Journal of Computational and Graphical Statistics, 13, 4, p. 907-929. http://dx.doi.org/10.1198/106186004X12803
TypeArticle accepté pour publication ou publié
Journal nameJournal of Computational and Graphical Statistics
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Abstract (EN)Importance sampling methods can be iterated like MCMC algorithms, while being more robust against dependence and starting values. The population Monte Carlo principle consists of iterated generations of importance samples, with importance functions depending on the previously generated importance samples. The advantage over MCMC algorithms is that the scheme is unbiased at any iteration and can thus be stopped at any time, while iterations improve the performances of the importance function, thus leading to an adaptive importance sampling. We illustrate this method on a mixture example with multiscale importance functions. A second example reanalyzes the ion channel model using an importance sampling scheme based on a hidden Markov representation, and compares population Monte Carlo with a corresponding MCMC algorithm.
Subjects / KeywordsMultiple scales; Ion channel model; importance sampling; hidden semi-Markov model; adaptive algorithms
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Barthelme, Simon; Beffy, Magali; Chopin, Nicolas; Doucet, Arnaud; Jacob, Pierre E.; Johansen, Adam M.; Marin, Jean-Michel; Robert, Christian P. (2011) Document de travail / Working paper