Compatible Prior Distributions for DAG models
Compatible Prior Distributions for DAG 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.
Group reference prior, FISHER INFORMATION MATRIX; GRAPHICAL MODEL; REFERENCE PRIOR; JEFFREY'S CONDITIONING; DIRECTED ACYCLIC GRAPH, Jeffreys conditioning, Fisher information matrix, Invariance, Reference conditioning, Reparameterization, Graphical model, Directed acyclic graph, Compatible prior, Bayes factor
Group reference prior, FISHER INFORMATION MATRIX; GRAPHICAL MODEL; REFERENCE PRIOR; JEFFREY'S CONDITIONING; DIRECTED ACYCLIC GRAPH, Jeffreys conditioning, Fisher information matrix, Invariance, Reference conditioning, Reparameterization, Graphical model, Directed acyclic graph, Compatible prior, Bayes factor
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