An Objective Bayesian Criterion to Determine Model Prior Probabilities
Cristiano Villa, Stephen Graham Walker · Scandinavian Journal of Statistics · 2015
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 theworthof having each model within the selection process. At the heart of the procedure is the measure of thisworthusing the Kullback–Leibler divergence between densities from different models.