
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
Type
Article accepté pour publication ou publiéDate
2004Journal name
Journal of Computational and Graphical StatisticsVolume
13Number
4Publisher
American Statistical Association
Pages
907-929
Publication identifier
Metadata
Show full item recordAbstract (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 / Keywords
Multiple scales; Ion channel model; importance sampling; hidden semi-Markov model; adaptive algorithmsRelated items
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Cappé, Olivier; Guillin, Arnaud; Marin, Jean-Michel; Robert, Christian P. (2002) Document de travail / Working paper
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Cappé, Olivier; Douc, Randal; Guillin, Arnaud; Marin, Jean-Michel; Robert, Christian P. (2008) Article accepté pour publication ou publié
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