Exploring the Maximally Sensitive Priors

Abstract We explore the Maximum Sensitivity method (MaxSen) of selecting priors for Bayesian inference in the face of severe lack of evidence (Konek, 2013). MaxSen minimizes the need to rely on the epistemic luck of true parameter values falling closely to their prior estimate.We explore the impact of the choice of the scoring method on the recommendations made by MaxSen. It turns out that if we use Kullback-Leibler Divergence (KLD) as a distance measure, the recommendations are the same as that of MaxEnt. We estimate how quickly the recommended prior distribution concentration increases with the planned sample size.