
Explaining the Perfect Sampler
Casella, George; Lavine, Michael; Robert, Christian P. (2001), Explaining the Perfect Sampler, The American Statistician, 55, 4, p. 299-305. http://dx.doi.org/10.1198/000313001753272240
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Article accepté pour publication ou publiéDate
2001Journal name
The American StatisticianVolume
55Number
4Publisher
American Statistical Association
Pages
299-305
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Metadata
Show full item recordAbstract (EN)
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.Subjects / Keywords
Coupling from the past; Fill's algorithm; Markov Chain Monte Carlo; Stochastic processesRelated items
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