DIMACS TR: 2002-17
Compatible Prior Distributions for DAG models
Authors: Alberto Roverato and Guido Consonni
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 DAG (Directed Acyclic Graph) model, using a conditioning
approach. We define a class of parameterisations consistent with the
modular structure of the DAG and derive a procedure, invariant within this
class, which we name reference conditioning.
Keywords: Bayes factor; Directed acyclic graph; Fisher information
matrix; Graphical model; Invariance; Jeffreys conditioning; Group
reference prior; Reference conditioning; Reparameterisation.
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