The Annals of Applied Probability

Perfect simulation for a class of positive recurrent Markov chains

Stephen B. Connor and Wilfrid S. Kendall

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Abstract

This paper generalizes the work of Kendall [Electron. Comm. Probab. 9 (2004) 140–151], which showed that perfect simulation, in the form of dominated coupling from the past, is always possible (although not necessarily practical) for geometrically ergodic Markov chains. Here, we consider the more general situation of positive recurrent chains and explore when it is possible to produce such a simulation algorithm for these chains. We introduce a class of chains which we name tame, for which we show that perfect simulation is possible.

Article information

Source
Ann. Appl. Probab., Volume 17, Number 3 (2007), 781-808.

Dates
First available in Project Euclid: 22 May 2007

Permanent link to this document
https://projecteuclid.org/euclid.aoap/1179839174

Digital Object Identifier
doi:10.1214/105051607000000032

Mathematical Reviews number (MathSciNet)
MR2326232

Zentralblatt MATH identifier
1125.60074

Subjects
Primary: 60J65: Brownian motion [See also 58J65] 65C05: Monte Carlo methods 68U20: Simulation [See also 65Cxx]

Keywords
CFTP domCFTP polynomial ergodicity Foster–Lyapunov condition Markov chain Monte Carlo perfect simulation

Citation

Connor, Stephen B.; Kendall, Wilfrid S. Perfect simulation for a class of positive recurrent Markov chains. Ann. Appl. Probab. 17 (2007), no. 3, 781--808. doi:10.1214/105051607000000032. https://projecteuclid.org/euclid.aoap/1179839174


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