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Widening and clustering techniques allowing the use of monotone CFTP algorithm

Author

Listed:
  • Bounnite Mohamed Yasser

    (Cadi Ayyad University, Faculty of Sciences Semlalia, B.P. 2390, Marrakesh, Morocco)

  • Nasroallah Abdelaziz

    (Cadi Ayyad University, Faculty of Sciences Semlalia, B.P. 2390, Marrakesh, Morocco)

Abstract

The standard Coupling From The Past (CFTP) algorithm is an interesting tool to sample from exact stationary distribution of a Markov chain. But it is very expensive in time consuming for large chains. There is a monotone version of CFTP, called MCFTP, that is less time consuming for monotone chains. In this work, we propose two techniques to get monotone chain allowing use of MCFTP: widening technique based on adding two fictitious states and clustering technique based on partitioning the state space in clusters. Usefulness and efficiency of our approaches are showed through a sample of Markov Chain Monte Carlo simulations.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:mcmeap:v:21:y:2015:i:4:p:301-312:n:6
    DOI: 10.1515/mcma-2015-0111
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    References listed on IDEAS

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    1. 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.
    2. G. Casella & K. L. Mengersen & C. P. Robert & D. M. Titterington, 2002. "Perfect samplers for mixtures of distributions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 777-790, October.
    3. 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.
    Full references (including those not matched with items on IDEAS)

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