Posterior probabilities for choosing a regression model

Abstract

SUMMARY A simple Bayesian formula for the posterior probability of one of several regression models is shown to be systematically misleading unless all models have the same number of parameters. Even in this case the use of improper priors leads to arbitrary inferences, as it does more generally. An alternative weighting for choosing a model is suggested and the relationship between significance tests and data-dependent priors mentioned.