IDEAS home Printed from https://ideas.repec.org/a/bpj/mcmeap/v21y2015i4p301-312n6.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/mcma-2015-0111
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/mcma-2015-0111?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Carsten Botts, 2013. "An accept-reject algorithm for the positive multivariate normal distribution," Computational Statistics, Springer, vol. 28(4), pages 1749-1773, August.
    7. 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.
    8. Juarez, Miguel A. & Steel, Mark F. J., 2006. "Model-based Clustering of non-Gaussian Panel Data," MPRA Paper 880, University Library of Munich, Germany.
    9. Sanjeena Subedi & Paul D. McNicholas, 2021. "A Variational Approximations-DIC Rubric for Parameter Estimation and Mixture Model Selection Within a Family Setting," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 89-108, April.
    10. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:mcmeap:v:21:y:2015:i:4:p:301-312:n:6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.