Compatible Priors for Causal Bayesian Networks

Guido Consonni, Valentina Leucari

Risultato della ricerca: Contributo in libroChapter

Abstract

We consider discrete causal DAG-models (or Bayesian Networks) wherein the ordering of the variables is fixed across model structures. Given a prior on the parameter space of a model we describe a method for deriving a compatible prior on the parameter space of a submodel. This allows to generate automatically compatible priors for model parameters starting from a single prior relative to the largest entertained model. Our method makes use of a general procedure for constructing compatible priors for causal DAG-models, named reference conditioning, which is invariant within a suitable class of re-parameterisations and is model intrinsic. We show that if the generating prior satisfies global parameter independence, so does the compatible prior; in addition, prior modularity holds. Further results are obtained when the starting prior is product Dirichlet. A simple illustration of the methodology, and comparisons with alternative methods, are presented.
Lingua originaleEnglish
Titolo della pubblicazione ospiteBayesian Statistics 7
EditorA. F. M. Smith Editor, Mike West D. Heckerman Editor, M. J. BAYARRI, A. P. DAWID, J. O. BERGER, D. HECKERMAN, A.F.M. SMITH, W. WEST, G G
Pagine596-607
Numero di pagine12
Stato di pubblicazionePubblicato - 2003

Keywords

  • Graphical model
  • Prior distribution

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