An Objective Bayesian Criterion to Determine Model Prior Probabilities

Abstract

We discuss the problem of selecting among alternative parametric models within the Bayesian framework. For model selection problems, which involve non-nested models, the common objective choice of a prior on the model space is the uniform distribution. The same applies to situations where the models are nested. It is our contention that assigning equal prior probability to each model is over simplistic. Consequently, we introduce a novel approach to objectively determine model prior probabilities, conditionally, on the choice of priors for the parameters of the models. The idea is based on the notion of the worth of having each model within the selection process. At the heart of the procedure is the measure of this worth using the Kullback-Leibler divergence between densities from different models.