Expected posterior prior distributions for model selection
J.M. Perez and J. Berger
We consider the problem of comparing parametric models
using a Bayesian approach. A new method of developing
prior distributions for the model parameters is presented,
called the expected posterior prior approach. The idea is
to define the priors for all models from a common underlying
predictive distribution, in such a way that the resulting priors
are amenable to modern MCMC computational techniques.
The approach has subjective Bayesian and
default Bayesian implementations, and overcomes the most significant
impediment to Bayesian model selection, that of ensuring that
prior distributions for the various models are appropriately
compatible.
Postscript File (285 kB)