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Explaining the Perfect Sampler
George Casella, Michael Lavine and Christian P. Robert
The American Statistician
Vol. 55, No. 4 (Nov., 2001), pp. 299-305
Stable URL: http://www.jstor.org/stable/2685691
Page Count: 7
You can always find the topics here!Topics: Markov chains, Random variables, Statistics, Ergodic theory, Simulations, Algorithms, Technical reports, Central limit theorem, Perceptron convergence procedure, Transition probabilities
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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.
The American Statistician © 2001 American Statistical Association