Work

Explaining the Perfect Sampler

Year: 2001

Type: article

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.

Source: The American Statistician

DOI: 10.1198/000313001753272240

Authors: George Casella, Michael Lavine, Christian P Robert

Institutions: Duke University, University of Florida, Université Paris Dauphine-PSL

Language: English

Topic: Markov Chains and Monte Carlo Methods

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2237290275

FWCI: 4.198

Cites: 6

Cited by: 46

Related to: 10