The Two-Stage Gibbs Sampler

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


The previous chapter presented the slice sampler, a special case of a Markov chain algorithm that did not need an Accept–Reject step to be valid, seemingly because of the uniformity of the target distribution. The reason why the slice sampler works is, however, unrelated to this uniformity and we will see in this chapter a much more general family of algorithms that function on the same principle. This principle is that of using the true conditional distributions associated with the target distribution to generate from that distribution.


Markov Chain Posterior Distribution Gibbs Sampler Joint Density 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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