IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v51y2022i6p1549-1568.html
   My bibliography  Save this article

Gibbs sampler and coordinate ascent variational inference: A set-theoretical review

Author

Listed:
  • Se Yoon Lee

Abstract

One of the fundamental problems in Bayesian statistics is the approximation of the posterior distribution. Gibbs sampler and coordinate ascent variational inference are renownedly utilized approximation techniques that rely on stochastic and deterministic approximations. In this paper, we define fundamental sets of densities frequently used in Bayesian inference. We shall be concerned with the clarification of the two schemes from the set-theoretical point of view. This new way provides an alternative mechanism for analyzing the two schemes endowed with pedagogical insights.

Suggested Citation

  • Se Yoon Lee, 2022. "Gibbs sampler and coordinate ascent variational inference: A set-theoretical review," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(6), pages 1549-1568, March.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:6:p:1549-1568
    DOI: 10.1080/03610926.2021.1921214
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2021.1921214
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2021.1921214?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.

    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:taf:lstaxx:v:51:y:2022:i:6:p:1549-1568. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/lsta .

    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.