Population Monte Carlo

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 sa...

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Bibliographic Details
Published in:Journal of computational and graphical statistics 2004-12, Vol.13 (4), p.907-929
Main Authors: Cappé, O, Guillin, A, Marin, J. M, Robert, C. P
Format: Article
Language:English
Subjects:
Adaptive algorithm
Algorithms
Approximation
Datasets
Hidden semi-Markov model
Histograms
Importance sampling
Ion channel model
Ion channels
Markov analysis
Markov chains
Markov models
Mathematics
Metropolitan areas
Modeling
Monte Carlo simulation
Multiple scales
Parametric models
Statistical discrepancies
Statistics
Studies
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Summary: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.
ISSN:1061-8600
1537-2715
DOI:10.1198/106186004X12803