Compatible prior distributions for directed acyclic graph models

Alberto Roverato and Guido Consonni

Journal of the Royal Statistical Society Series B, 2004, vol. 66, issue 1, 47-61

Abstract: Summary. The application of certain Bayesian techniques, such as the Bayes factor and model averaging, requires the specification of prior distributions on the parameters of alternative models. We propose a new method for constructing compatible priors on the parameters of models nested in a given directed acyclic graph model, using a conditioning approach. We define a class of parameterizations that is consistent with the modular structure of the directed acyclic graph and derive a procedure, that is invariant within this class, which we name reference conditioning.

Date: 2004
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