IDEAS home Printed from https://ideas.repec.org/a/bla/stanee/v59y2005i1p3-15.html
   My bibliography  Save this article

Posterior model probabilities via path‐based pairwise priors

Author

Listed:
  • James O. Berger
  • German Molina

Abstract

We focus on Bayesian model selection for the variable selection problem in large model spaces. The challenge is to search the huge model space adequately, while accurately approximating model posterior probabilities for the visited models. The issue of choice of prior distributions for the visited models is also important.

Suggested Citation

  • James O. Berger & German Molina, 2005. "Posterior model probabilities via path‐based pairwise priors," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 59(1), pages 3-15, February.
    • Handle: RePEc:bla:stanee:v:59:y:2005:i:1:p:3-15
    DOI: 10.1111/j.1467-9574.2005.00275.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9574.2005.00275.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9574.2005.00275.x?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
    ---><---

    Citations

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


    Cited by:

    1. Davide Altomare & Guido Consonni & Luca La Rocca, 2011. "Objective Bayesian Search of Gaussian DAG Models with Non-local Priors," Quaderni di Dipartimento 140, University of Pavia, Department of Economics and Quantitative Methods.
    2. Gonzalo García-Donato & María Eugenia Castellanos & Alicia Quirós, 2021. "Bayesian Variable Selection with Applications in Health Sciences," Mathematics, MDPI, vol. 9(3), pages 1-16, January.
    3. Li Ma, 2015. "Scalable Bayesian Model Averaging Through Local Information Propagation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 795-809, June.
    4. Davide Altomare & Guido Consonni & Luca La Rocca, 2013. "Objective Bayesian Search of Gaussian Directed Acyclic Graphical Models for Ordered Variables with Non-Local Priors," Biometrics, The International Biometric Society, vol. 69(2), pages 478-487, June.
    5. Joyee Ghosh & Andrew E. Ghattas, 2015. "Bayesian Variable Selection Under Collinearity," The American Statistician, Taylor & Francis Journals, vol. 69(3), pages 165-173, August.
    6. Guido Consonni & Luca La Rocca, 2010. "Moment Priors for Bayesian Model Choice with Applications to Directed Acyclic Graphs," Quaderni di Dipartimento 115, University of Pavia, Department of Economics and Quantitative Methods.

    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:bla:stanee:v:59:y:2005:i:1:p:3-15. 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.

    We have no bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0039-0402 .

    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.