IDEAS home Printed from https://ideas.repec.org/a/spr/aistmt/v61y2009i4p949-967.html
   My bibliography  Save this article

Metropolis–Hastings Algorithms with acceptance ratios of nearly 1

Author

Listed:
  • Kengo Kamatani

Abstract

No abstract is available for this item.

Suggested Citation

  • Kengo Kamatani, 2009. "Metropolis–Hastings Algorithms with acceptance ratios of nearly 1," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(4), pages 949-967, December.
    • Handle: RePEc:spr:aistmt:v:61:y:2009:i:4:p:949-967
    DOI: 10.1007/s10463-008-0180-6
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10463-008-0180-6
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10463-008-0180-6?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. G. O. Roberts & O. Stramer, 2002. "Langevin Diffusions and Metropolis-Hastings Algorithms," Methodology and Computing in Applied Probability, Springer, vol. 4(4), pages 337-357, December.
    2. Søren F. Jarner & Gareth O. Roberts, 2007. "Convergence of Heavy‐tailed Monte Carlo Markov Chain Algorithms," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 781-815, December.
    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. Dalalyan, Arnak S. & Karagulyan, Avetik, 2019. "User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient," Stochastic Processes and their Applications, Elsevier, vol. 129(12), pages 5278-5311.
    2. Peter Neal & Gareth Roberts, 2011. "Optimal Scaling of Random Walk Metropolis Algorithms with Non-Gaussian Proposals," Methodology and Computing in Applied Probability, Springer, vol. 13(3), pages 583-601, September.
    3. Ruben Loaiza-Maya & Didier Nibbering & Dan Zhu, 2023. "Hybrid unadjusted Langevin methods for high-dimensional latent variable models," Papers 2306.14445, arXiv.org.
    4. Radu Herbei & Rajib Paul & L Mark Berliner, 2017. "Applying diffusion-based Markov chain Monte Carlo," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-14, March.
    5. Dang, Khue-Dung & Quiroz, Matias & Kohn, Robert & Tran, Minh-Ngoc & Villani, Mattias, 2019. "Hamiltonian Monte Carlo with Energy Conserving Subsampling," Working Paper Series 372, Sveriges Riksbank (Central Bank of Sweden).
    6. Jakubowski, Adam & Truszczyński, Patryk, 2018. "Quenched phantom distribution functions for Markov chains," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 79-83.
    7. Kamatani, Kengo, 2020. "Random walk Metropolis algorithm in high dimension with non-Gaussian target distributions," Stochastic Processes and their Applications, Elsevier, vol. 130(1), pages 297-327.
    8. Matti Vihola & Jouni Helske & Jordan Franks, 2020. "Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1339-1376, December.
    9. Gunawan, David & Dang, Khue-Dung & Quiroz, Matias & Kohn, Robert & Tran, Minh-Ngoc, 2019. "Subsampling Sequential Monte Carlo for Static Bayesian Models," Working Paper Series 371, Sveriges Riksbank (Central Bank of Sweden).
    10. Samuel Livingstone, 2021. "Geometric Ergodicity of the Random Walk Metropolis with Position-Dependent Proposal Covariance," Mathematics, MDPI, vol. 9(4), pages 1-14, February.
    11. Robert D. Skeel & Carsten Hartmann, 2021. "Choice of damping coefficient in Langevin dynamics," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(9), pages 1-13, September.
    12. Bédard, Mylène, 2017. "Hierarchical models: Local proposal variances for RWM-within-Gibbs and MALA-within-Gibbs," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 231-246.

    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:spr:aistmt:v:61:y:2009:i:4:p:949-967. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.