The Dirichlet Process as sampling distribution

Abstract

The Dirichlet process (DP) is the most common bayesian nonparametric prior, however, its properties as sampling distribution have not been studied nor inference on its parameters. Here we use the DP as a data generating model and make bayesian inference on its centering measure and precision parameter. We illustrate with a sequence of histograms as observed data. In particular, we consider simulated and real datasets.