Objective Bayesian Model Discrimination in Follow-up Experimental Designs

TL;DR

This paper introduces an objective Bayesian method for follow-up experimental designs that uses tailored priors and a model selection criterion based on Kullback-Leibler divergence, improving upon previous subjective approaches.

Contribution

It develops a fully Bayesian objective approach for model discrimination in follow-up experiments using a novel divergence-based criterion.

Findings

Method performs well on real data

Outperforms previous subjective prior methods

Provides clearer model discrimination results

Abstract

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 a fully Bayes objective approach to follow-up designs, using prior distributions suitably tailored to model selection. We adopt a model 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 produces results which compare favorably to previous analyses based on subjective weakly informative priors.