The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Population Monte Carlo
Open accessAbstract
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.
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Citations
Cites
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
Cited by
Sequential Monte Carlo Samplers
Del Moral, P., Doucet, A., & Jasra, A. (2006) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2006.00553.x · Cited by 1,239
Adaptive approximate Bayesian computation
Beaumont, M. A., Cornuet, J.-M., Marin, J.-M., & Robert, C. P. (2009) · Biometrika · doi:10.1093/biomet/asp052 · Cited by 614
“While this method is based upon the theoretical works of Del Moral et al. (2006), the application to approximate Bayesian computation results in a bias in the approximation to the posterior.”
MethodSelf-citation
Metadata
- Authors
- Cappé, O., Guillin, A., Marin, J. M., & Robert, C. P.
- Venue
- Journal of Computational and Graphical Statistics
- Publisher
- Informa UK Limited
- Published
- 2004-12
- Volume
- 13
- Issue
- 4
- Pages
- 907-929
- ISSN
- 1061-8600 (ISSN-L)
- 1537-2715
- OpenAlex
- W1979969656
- Cited by
- 472
- References
- 1
- FWCI
- 12.47
- Type
- journal article
- Language
- en
- Open access
- green
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