PreprintCC BY 4.0arXiv
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.
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BibTeX
@misc{nietobarajas2026dirichlet,
title = {{The Dirichlet Process as sampling distribution}},
author = {Luis E. Nieto-Barajas},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2607.26185},
eprint = {2607.26185},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.26185},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - The Dirichlet Process as sampling distribution
AU - Luis E. Nieto-Barajas
PY - 2026
DA - 2026/10/27
PB - arXiv
DO - 10.48550/arxiv.2607.26185
UR - https://arxiv.org/abs/2607.26185
AB - 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.
ER -