Compatible prior distributions for directed acyclic graph models
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. Copyright 2004 Royal Statistical Society.
Year of publication: |
2004
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Authors: | Roverato, Alberto ; Consonni, Guido |
Published in: |
Journal of the Royal Statistical Society Series B. - Royal Statistical Society - RSS, ISSN 1369-7412. - Vol. 66.2004, 1, p. 47-61
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Publisher: |
Royal Statistical Society - RSS |
Saved in:
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