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Compatible prior distributions for directed acyclic graph models
Author(s) -
Roverato Alberto,
Consonni Guido
Publication year - 2004
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/j.1467-9868.2004.00431.x
Subject(s) - directed acyclic graph , prior probability , moral graph , bayes factor , computer science , directed graph , bayesian probability , graph , class (philosophy) , modular design , bayes' theorem , mathematics , algorithm , theoretical computer science , artificial intelligence , voltage graph , programming language , line graph
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.