The Dirichlet Process as sampling distribution
arXiv:2607.26185
summary
The paper treats the Dirichlet process as a data‑generating model and develops Bayesian inference for its centering measure and precision parameter, demonstrating the approach with simulated and real histogram data.
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.
Topics & keywords
#bayesian nonparametrics#dirichlet process#sampling distribution#parameter inference#centering measure#precision parameterDirichlet processBayesian inferencenonparametric priorprecision parametercentering measurehistogram