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 originale | Inglese |
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Titolo della pubblicazione ospite | Bayesian Statistics 7 |
Editor | A. 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 |
Pagine | 596-607 |
Numero di pagine | 12 |
Stato di pubblicazione | Pubblicato - 2003 |
Keywords
- Graphical model
- Prior distribution