A Bayesian Approach To Histogram Comparison

Abstract

Determining if two histograms are consistent, whether they have been drawn from the same underlying distribution or not, is a common problem in physics. Existing approaches are not only limited in power but also inapplicable to histograms filled with importance weights, a common feature of Monte Carlo simulations. From a Bayesian perspective, the comparison between a single underlying distribution and two underlying distributions is readily solved within the context of model comparison. I introduce an implementation of Bayesian model comparison to the problem, including the extension to importance sampling.