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Normative selection of Bayesian networks

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  • 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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. 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.
    2. 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.

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