• xmlui.mirage2.page-structure.header.title
    • français
    • English
  • Help
  • Login
  • Language 
    • Français
    • English
View Item 
  •   BIRD Home
  • CEREMADE (UMR CNRS 7534)
  • CEREMADE : Publications
  • View Item
  •   BIRD Home
  • CEREMADE (UMR CNRS 7534)
  • CEREMADE : Publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Browse

BIRDResearch centres & CollectionsBy Issue DateAuthorsTitlesTypeThis CollectionBy Issue DateAuthorsTitlesType

My Account

LoginRegister

Statistics

Most Popular ItemsStatistics by CountryMost Popular Authors
Thumbnail

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

View/Open
2002-34.ps (3.298Mb)
popMC.PDF (1.150Mb)
Type
Article accepté pour publication ou publié
Date
2004
Journal name
Journal of Computational and Graphical Statistics
Volume
13
Number
4
Publisher
American Statistical Association
Pages
907-929
Publication identifier
http://dx.doi.org/10.1198/106186004X12803
Metadata
Show full item record
Author(s)
Cappé, Olivier cc
Guillin, Arnaud
Marin, Jean-Michel cc
Robert, Christian P.
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 / Keywords
Multiple scales; Ion channel model; importance sampling; hidden semi-Markov model; adaptive algorithms
JEL
C11 - Bayesian Analysis: General

Related items

Showing items related by title and author.

  • Thumbnail
    Population Monte Carlo for Ion Channel Restoration 
    Cappé, Olivier; Guillin, Arnaud; Marin, Jean-Michel; Robert, Christian P. (2002) Document de travail / Working paper
  • Thumbnail
    Minimum variance importance sampling via Population Monte Carlo 
    Douc, Randal; Guillin, Arnaud; Marin, Jean-Michel; Robert, Christian P. (2007) Article accepté pour publication ou publié
  • Thumbnail
    Adaptive Importance Sampling in General Mixture Classes 
    Cappé, Olivier; Douc, Randal; Guillin, Arnaud; Marin, Jean-Michel; Robert, Christian P. (2008) Article accepté pour publication ou publié
  • Thumbnail
    On variance stabilisation in population Monte Carlo by double Rao-Blackwellisation 
    Robert, Christian P.; Marin, Jean-Michel; Iacobucci, Alessandra (2010) Article accepté pour publication ou publié
  • Thumbnail
    Discussions on "Riemann manifold Langevin and Hamiltonian Monte Carlo methods" 
    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
Dauphine PSL Bibliothèque logo
Place du Maréchal de Lattre de Tassigny 75775 Paris Cedex 16
Phone: 01 44 05 40 94
Contact
Dauphine PSL logoEQUIS logoCreative Commons logo