IDEAS home Printed from https://ideas.repec.org/h/ito/pchaps/201842.html
   My bibliography  Save this book chapter

A Brief Tour of Bayesian Sampling Methods

In: Bayesian Inference on Complicated Data

Author

Listed:
  • Michelle Yongmei Wang
  • Trevor Park

Abstract

Unlike in the past, the modern Bayesian analyst has many options for approximating intractable posterior distributions. This chapter briefly summarizes the class of posterior sampling methods known as Markov chain Monte Carlo, a type of dependent sampling strategy. Varieties of algorithms exist for constructing chains, and we review some of them here. Such methods are quite flexible and are now used routinely, even for relatively complicated statistical models. In addition, extensions of the algorithms have been developed for various goals. General-purpose software is currently also available to automate the construction of samplers, freeing the analyst to focus on model formulation and inference.

Suggested Citation

  • Michelle Yongmei Wang & Trevor Park, 2020. "A Brief Tour of Bayesian Sampling Methods," Chapters, in: Niansheng Tang (ed.), Bayesian Inference on Complicated Data, IntechOpen.
    • Handle: RePEc:ito:pchaps:201842
    DOI: 10.5772/intechopen.91451
    as

    Download full text from publisher

    File URL: https://www.intechopen.com/chapters/71603
    Download Restriction: no
    File URL: https://libkey.io/10.5772/intechopen.91451?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
    ---><---

    More about this item

    Keywords

    Markov chain Monte Carlo; Gibbs sampler; slice sampler; Metropolis-Hastings; Hamiltonian Monte Carlo; cluster sampling; JAGS; Stan;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

    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:ito:pchaps:201842. 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: Slobodan Momcilovic (email available below). General contact details of provider: http://www.intechopen.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.