IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v93y2005i2p340-357.html
   My bibliography  Save this article

Normative selection of Bayesian networks

Author

Listed:
  • Sebastiani, Paola
  • Ramoni, Marco

Abstract

This paper presents a Bayesian decision theoretic foundation to the selection of a Bayesian network from data. We introduce the class of disintegrable loss functions to diversify the loss incurred in choosing different models. Disintegrable loss functions can iteratively be built from simple 0-L loss functions over pair-wise model comparisons and decompose the search for the model with minimum risk into a sequence of local searches, thus retaining the modularity of the model selection procedures for Bayesian networks.

Suggested Citation

  • Sebastiani, Paola & Ramoni, Marco, 2005. "Normative selection of Bayesian networks," Journal of Multivariate Analysis, Elsevier, vol. 93(2), pages 340-357, April.
    • Handle: RePEc:eee:jmvana:v:93:y:2005:i:2:p:340-357
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047-259X(04)00043-0
    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.

    References listed on IDEAS

    as
    1. D. J. Spiegelhalter, 1999. "Surgical audit: statistical lessons from Nightingale and Codman," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(1), pages 45-58.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Fernandes, Jose A. & Irigoien, Xabier & Goikoetxea, Nerea & Lozano, Jose A. & Inza, Iñaki & Pérez, Aritz & Bode, Antonio, 2010. "Fish recruitment prediction, using robust supervised classification methods," Ecological Modelling, Elsevier, vol. 221(2), pages 338-352.
    2. Donatello Telesca & Peter Müller & Steven M. Kornblau & Marc A. Suchard & Yuan Ji, 2012. "Modeling Protein Expression and Protein Signaling Pathways," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1372-1384, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Consonni, Guido & Veronese, Piero & Gutiérrez-Peña, Eduardo, 2004. "Reference priors for exponential families with simple quadratic variance function," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 335-364, February.
    2. Nicholas Longford & D. B. Rubin, 2006. "Performance assessment and league tables. Comparing like with like," Economics Working Papers 994, Department of Economics and Business, Universitat Pompeu Fabra.
    3. Geng, Zhi & He, Yang-Bo & Wang, Xue-Li & Zhao, Qiang, 2003. "Bayesian method for learning graphical models with incompletely categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 175-192, October.
    4. Peter C. Smith & Andrew D. Street, 2013. "On The Uses Of Routine Patient‐Reported Health Outcome Data," Health Economics, John Wiley & Sons, Ltd., vol. 22(2), pages 119-131, February.
    5. Roverato, Alberto & Paterlini, Sandra, 2004. "Technological modelling for graphical models: an approach based on genetic algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 323-337, September.
    6. Jan Beyersmann & Christine Schrade, 2017. "Florence Nightingale, William Farr and competing risks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 285-293, January.
    7. Gwyn Bevan & Richard Hamblin, 2009. "Hitting and missing targets by ambulance services for emergency calls: effects of different systems of performance measurement within the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 161-190, January.
    8. Scott, Steven L., 2004. "A Bayesian paradigm for designing intrusion detection systems," Computational Statistics & Data Analysis, Elsevier, vol. 45(1), pages 69-83, February.
    9. David I. Ohlssen & Linda D. Sharples & David J. Spiegelhalter, 2007. "A hierarchical modelling framework for identifying unusual performance in health care providers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 865-890, October.

    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:93:y:2005:i:2:p:340-357. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.