Imprecise Dirichlet Process with application to the hypothesis test on the probability that X< Y
Alessio Benavoli, Francesca Mangili, Fabrizio Ruggeri, Marco Zaffalon · arXiv (Cornell University) · 2014
The Dirichlet process (DP) is one of the most popular Bayesian nonparametric models. An open problem with the DP is how to choose its infinite dimensional parameter (base measure) in case of lack of prior information. In this work we present the Imprecise DP (IDP) -- a prior near-ignorance DP-based model that does not require any choice of this probability measure. It consists of a class of DPs obtained by letting the normalized base measure of the DP vary in the set of all probability measures. We discuss the tight connections of this approach with Bayesian robustness and in particular prior near-ignorance modeling via sets of probabilities. We use this model to perform a Bayesian hypothesis test on the probability P(X