Electronic Communications in Probability

Fill's Algorithm for Absolutely Continuous Stochastically Monotone Kernels

Motoya Machida

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Abstract

Fill, Machida, Murdoch, and Rosenthal (2000) presented their algorithm and its variants to extend the perfect sampling algorithm of Fill (1998) to chains on continuous state spaces. We consider their algorithm for absolutely continuous stochastically monotone kernels, and show the correctness of the algorithm under a set of certain regularity conditions. These conditions succeed in relaxing the previously known hypotheses sufficient for their algorithm to apply.

Article information

Source
Electron. Commun. Probab., Volume 7 (2002), paper no. 15, 141-155.

Dates
Accepted: 5 August 2002
First available in Project Euclid: 16 May 2016

Permanent link to this document
https://projecteuclid.org/euclid.ecp/1463434782

Digital Object Identifier
doi:10.1214/ECP.v7-1056

Mathematical Reviews number (MathSciNet)
MR1937900

Zentralblatt MATH identifier
1010.60068

Subjects
Primary: 60J10: Markov chains (discrete-time Markov processes on discrete state spaces) 68U20: Simulation [See also 65Cxx]
Secondary: 60G40: Stopping times; optimal stopping problems; gambling theory [See also 62L15, 91A60] 65C05: Monte Carlo methods 65C10: Random number generation 65C40: Computational Markov chains

Keywords
Markov chain Monte Carlo Fill's algorithm perfect sampling exact sampling rejection sampling stochastic monotonicity partially ordered set monotone coupling absolutely continuous Markov kernel regularity conditions

Rights
This work is licensed under aCreative Commons Attribution 3.0 License.

Citation

Machida, Motoya. Fill's Algorithm for Absolutely Continuous Stochastically Monotone Kernels. Electron. Commun. Probab. 7 (2002), paper no. 15, 141--155. doi:10.1214/ECP.v7-1056. https://projecteuclid.org/euclid.ecp/1463434782


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References

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