Iterated and Sequential Importance Sampling

  • Christian P. Robert
  • George Casella
Part of the Springer Texts in Statistics book series (STS)


This chapter gives an introduction to sequential simulation methods, a collection of algorithms that build both on MCMC methods and importance sampling, with importance sampling playing a key role. We will see the relevance of importance sampling and the limitations of standard MCMC methods in many settings, as we try to make the reader aware of important and ongoing developments in this area. In particular, we present an introduction. to Population Monte Carlo (Section 14.4), which extends these notions to a more general case, and subsumes MCMC methods


Particle Filter Importance Sampling Importance Weight MCMC Method Target Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2004

Authors and Affiliations

  • Christian P. Robert
    • 1
  • George Casella
    • 2
  1. 1.CEREMADEUniversité Paris DauphineParis Cedex 16France
  2. 2.Department of StatisticsUniversity of FloridaGainesvilleUSA

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