On the Sensitivity of Bayes Factors to the Prior Distributions

The Bayes factoris a Bayesian statistician's tool for model selection. Bayes factors can be highly sensitive to the prior distributions used for the parameters of the models under consideration. We discuss an approach for studying the sensitivity of the Bayes factor to the prior distributions for the parameters in the models being compared. The approach is found to be extremely useful for nested models; it has a graphical flavor making it more attractive than other common approaches to sensitivity analysis for Bayes factors.