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Perfect slice samplers

Author

Listed:
  • A. Mira
  • J. Møller
  • G. O. Roberts

Abstract

Perfect sampling allows the exact simulation of random variables from the stationary measure of a Markov chain. By exploiting monotonicity properties of the slice sampler we show that a perfect version of the algorithm can be easily implemented, at least when the target distribution is bounded. Various extensions, including perfect product slice samplers, and examples of applications are discussed.

Suggested Citation

  • A. Mira & J. Møller & G. O. Roberts, 2001. "Perfect slice samplers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 593-606.
    • Handle: RePEc:bla:jorssb:v:63:y:2001:i:3:p:593-606
    DOI: 10.1111/1467-9868.00301
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    Citations

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    Cited by:

    1. Carsten Botts, 2013. "An accept-reject algorithm for the positive multivariate normal distribution," Computational Statistics, Springer, vol. 28(4), pages 1749-1773, August.
    2. van Lieshout, M.N.M. & Stoica, R.S., 2006. "Perfect simulation for marked point processes," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 679-698, November.
    3. Nasroallah Abdelaziz & Bounnite Mohamed Yasser, 2019. "A kind of dual form for coupling from the past algorithm, to sample from Markov chain steady-state probability," Monte Carlo Methods and Applications, De Gruyter, vol. 25(4), pages 317-327, December.
    4. Fakhouri H. & Nasroallah A., 2009. "On the simulation of Markov chain steady-state distribution using CFTP algorithm," Monte Carlo Methods and Applications, De Gruyter, vol. 15(2), pages 91-105, January.
    5. Claes Fornell & Paul Damien & Marcin Kacperczyk & Michel Wedel, 2018. "Does Aggregate Buyer Satisfaction affect Household Consumption Growth?," DOCFRADIS Working Papers 1802, Catedra Fundación Ramón Areces de Distribución Comercial, revised Jun 2018.
    6. Christian P. Robert & Gareth Roberts, 2021. "Rao–Blackwellisation in the Markov Chain Monte Carlo Era," International Statistical Review, International Statistical Institute, vol. 89(2), pages 237-249, August.
    7. Marcin Kacperczyk & Paul Damien & Stephen G. Walker, 2013. "A new class of Bayesian semi-parametric models with applications to option pricing," Quantitative Finance, Taylor & Francis Journals, vol. 13(6), pages 967-980, May.
    8. Bounnite Mohamed Yasser & Nasroallah Abdelaziz, 2015. "Widening and clustering techniques allowing the use of monotone CFTP algorithm," Monte Carlo Methods and Applications, De Gruyter, vol. 21(4), pages 301-312, December.
    9. Tan, Ming & Tian, Guo-Liang & Wang Ng, Kai, 2006. "Hierarchical models for repeated binary data using the IBF sampler," Computational Statistics & Data Analysis, Elsevier, vol. 50(5), pages 1272-1286, March.

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