Population Monte Carlo
Year: 2004
Type: article
Abstract: 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.
Authors: O Cappé, A Guillin, J. M Marin, C. P Robert
Institutions: Télécom Paris, Centre National de la Recherche Scientifique, Centre de Recherche en Mathématiques de la Décision, Centre de Recherche en Économie et Statistique, Laboratoire Traitement et Communication de l’Information, Université Paris Dauphine-PSL, Laboratoire de Mathématiques Blaise Pascal, University of Warwick
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1979969656