Year: 2026
Type: preprint
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.
Source: arXiv (Cornell University)
Author: Nieto-Barajas, Luis E.
Institution: Instituto Tecnológico Autónomo de México
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Brown University
OpenAlex ID: W7171864530
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