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An initial screening experiment may lead to ambiguous conclusions regardingthe factors which are active in explaining the variation of an outcomevariable: thus adding follow-up runs becomes necessary. We propose a fullyBayes objective approach to follow-up designs, using prior distributionssuitably tailored to model selection. We adopt a model criterion based on aweighted average of Kullback-Leibler divergences between predictivedistributions for all possible pairs of models. When applied to real data, ourmethod produces results which compare favorably to previous analyses based onsubjective weakly informative priors.

Objective Bayesian Model Discrimination in Follow-up Experimental Designs