IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v37y1991i2p197-222.html
   My bibliography  Save this article

Comparing sweep strategies for stochastic relaxation

Author

Listed:
  • Amit, Y.
  • Grenander, U.

Abstract

The rate of convergence of various sweep strategies of stochastic relaxation for simulating multivariate Gaussian measures are calculated and compared. Each sweep strategy prescribes a method for chosing which coordinates of the random vector are to be updated. Deterministic sweep strategies in which the coordinates are updated according to a fixed order are compared to random strategies in which the coordinate to be updated is chosen through some random mechanism. In addition block updating, in which a few coordinates are updated simultaneously, is compared to single coordinate updating.

Suggested Citation

  • Amit, Y. & Grenander, U., 1991. "Comparing sweep strategies for stochastic relaxation," Journal of Multivariate Analysis, Elsevier, vol. 37(2), pages 197-222, May.
  • Handle: RePEc:eee:jmvana:v:37:y:1991:i:2:p:197-222
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0047-259X(91)90080-L
    Download Restriction: Full text for ScienceDirect subscribers only

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

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Levine, Richard A. & Casella, George, 2006. "Optimizing random scan Gibbs samplers," Journal of Multivariate Analysis, Elsevier, vol. 97(10), pages 2071-2100, November.
    2. Yuen, Wai Kong, 2000. "Applications of geometric bounds to the convergence rate of Markov chains on," Stochastic Processes and their Applications, Elsevier, vol. 87(1), pages 1-23, May.
    3. Rosenthal, Jeffrey S., 1996. "Markov chain convergence: From finite to infinite," Stochastic Processes and their Applications, Elsevier, vol. 62(1), pages 55-72, March.
    4. Hwang, Chii-Ruey & Sheu, Shuenn-Jyi, 1998. "On the Geometrical Convergence of Gibbs Sampler inRd," Journal of Multivariate Analysis, Elsevier, vol. 66(1), pages 22-37, July.
    5. Neil Shephard & Michael K Pitt, 1995. "Likelihood analysis of non-Gaussian parameter driven models," Economics Papers 15 & 108., Economics Group, Nuffield College, University of Oxford.
    6. Tervonen, Tommi & van Valkenhoef, Gert & Baştürk, Nalan & Postmus, Douwe, 2013. "Hit-And-Run enables efficient weight generation for simulation-based multiple criteria decision analysis," European Journal of Operational Research, Elsevier, vol. 224(3), pages 552-559.

    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:eee:jmvana:v:37:y:1991:i:2:p:197-222. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.

    We have no references for this item. You can help adding them by using 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.