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Explaining the Perfect Sampler

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Author Info

  • Casella, George
  • Lavine, Michael
  • Robert, Christian P.
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    Abstract

    In 1996, Propp and Wilson introduced coupling from the past (CFTP), an algorithm for generating a sample from the exact stationary distribution of a Markov chain. In 1998, Fill proposed another so–called perfect sampling algorithm. These algorithms have enormous potential in Markov Chain Monte Carlo (MCMC) problems because they eliminate the need to monitor convergence and mixing of the chain. This article provides a brief introduction to the algorithms, with an emphasis on understanding rather than technical detail.

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    File URL: http://basepub.dauphine.fr/xmlui/bitstream/123456789/6189/2/2000-49.pdf
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    Bibliographic Info

    Paper provided by Université Paris-Dauphine in its series Open Access publications from Université Paris-Dauphine with number urn:hdl:123456789/6189.

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    Date of creation: 2001
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    Publication status: Published in American Statistician (2001) v.55, p.299-305
    Handle: RePEc:ner:dauphi:urn:hdl:123456789/6189

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    Web page: http://www.dauphine.fr/en/welcome.html

    Related research

    Keywords: Coupling from the past; Fill's algorithm; Markov Chain Monte Carlo; Stochastic processes;

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    Cited by:
    1. Tan, Ming & Tian, Guo-Liang & Wang Ng, Kai, 2006. "Hierarchical models for repeated binary data using the IBF sampler," Computational Statistics & Data Analysis, Elsevier, vol. 50(5), pages 1272-1286, March.

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