Objective Bayesian model discrimination in follow-up experimental designs

An initial screening experiment may lead to ambiguous conclusions regarding the factors which are active in explaining the variation of an outcome variable: thus adding follow-up runs becomes necessary. We propose an objective Bayesian approach to follow-up designs, using prior distributions suitably tailored to model selection. We adopt a model discrimination criterion based on a weighted average of Kullback-Leibler divergences between predictive distributions for all possible pairs
of models. When applied to real data, our method, which is fully automatic, produces results which compare favorably to previous analyses based on subjective priors. Supplementary materials are available online.