IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-82808-0_3.html
   My bibliography  Save this book chapter

Graphical Modelling and Hierarchical Models

In: An Introduction to Bayesian Inference, Methods and Computation

Author

Listed:
  • Nick Heard

    (Imperial College London)

Abstract

In many contexts, straightforward exchangeability can be a useful simplifying assumption for specifying the joint probability distribution of random variables. But sometimes an individual will require more complex structures of statistical dependence between random quantities to properly represent their beliefs. Graphical models provide a useful framework for characterising joint distributions for random variables, putting primary focus on characterising uncertainty in the dependency structure amongst the variables. Much of the material in this chapter is drawn from Barber (2012) and related resources. Before introducing graphical models, some basic graph concepts and definitions are required to provide a language for relating probability distributions to graphs.

Suggested Citation

  • Nick Heard, 2021. "Graphical Modelling and Hierarchical Models," Springer Books, in: An Introduction to Bayesian Inference, Methods and Computation, chapter 3, pages 23-32, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-82808-0_3
    DOI: 10.1007/978-3-030-82808-0_3
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    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:spr:sprchp:978-3-030-82808-0_3. 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: 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.