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Global prior distributions for the analysis of discrete graphical models

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Summary

We propose a new class of prior distributions for the analysis of discrete graphical models. Such a class, obtained following a conditional approach, generalizes the hyper Dirichlet distributions of Dawid and Lauritzen (1993), since it can be extended to non decomposable graphical models. The two classes are compared in terms of model selection, with an application to a medical data-set illustrating the performance of the two resulting procedures. The proposed class turns out to select simpler, more par-simonious structures.

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Authors and Affiliations

  1. Università di Pavia, Pavia, Italy

    Paolo Giudici

  2. Università di Trento, Trento, Italy

    Claudia Tarantola

Authors
  1. Paolo Giudici
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  2. Claudia Tarantola
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Correspondence to Paolo Giudici.

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Giudici, P., Tarantola, C. Global prior distributions for the analysis of discrete graphical models. J. It. Statist. Soc. 5, 129–147 (1996). https://doi.org/10.1007/BF02589585

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  • DOI: https://doi.org/10.1007/BF02589585

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