Explaining the Perfect Sampler
AbstractIn 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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Bibliographic InfoPaper provided by Université Paris-Dauphine in its series Open Access publications from Université Paris-Dauphine with number urn:hdl:123456789/6189.
Date of creation: 2001
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Publication status: Published in American Statistician (2001) v.55, p.299-305
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Coupling from the past; Fill's algorithm; Markov Chain Monte Carlo; Stochastic processes;
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