IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v85y2003i2p253-266.html
   My bibliography  Save this article

Bayesian graphical model determination using decision theory

Author

Listed:
  • Corander, Jukka

Abstract

Bayesian model determination in the complete class of graphical models is considered using a decision theoretic framework within the regular exponential family. The complete class contains both decomposable and non-decomposable graphical models. A utility measure based on a logarithmic score function is introduced under reference priors for the model parameters. The logarithmic utility of a model is decomposed into predictive performance and relative complexity. Axioms of decision theory lead to the judgement of the plausibility of a model in terms of the posterior expected utility. This quantity has an analytic expression for decomposable models when certain reference priors are used and the exponential family is closed under marginalization. For non-decomposable models, a simulation consistent estimate of the expectation can be obtained. Both real and simulated data sets are used to illustrate the introduced methodology.

Suggested Citation

  • Corander, Jukka, 2003. "Bayesian graphical model determination using decision theory," Journal of Multivariate Analysis, Elsevier, vol. 85(2), pages 253-266, May.
    • Handle: RePEc:eee:jmvana:v:85:y:2003:i:2:p:253-266
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047-259X(02)00033-7
    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. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2012. "Bayesian Graphical Models for Structural Vector Autoregressive Processes," Working Papers 2012:36, Department of Economics, University of Venice "Ca' Foscari".
    2. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2016. "Bayesian Graphical Models for STructural Vector Autoregressive Processes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(2), pages 357-386, March.

    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:85:y:2003:i:2:p:253-266. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.