dirichletprocess: An R Package for Fitting Complex Bayesian Nonparametric Models

Fuente: arXiv
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Bibliographic Details
Main Authors: Ross, Gordon J., Markwick, Dean, Tiwari, Priyanshu
Format: Preprint
Published: 2026
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author Ross, Gordon J.
Markwick, Dean
Tiwari, Priyanshu
author_facet Ross, Gordon J.
Markwick, Dean
Tiwari, Priyanshu
contents The dirichletprocess package provides software for creating flexible Dirichlet process objects. Users can perform nonparametric Bayesian analysis using Dirichlet processes without the need to program their own inference algorithms. Instead, the user can utilise our pre-built models or specify their own models whilst allowing the dirichletprocess package to handle the Markov chain Monte Carlo sampling. Our Dirichlet process objects can act as building blocks for a variety of statistical models including: density estimation, clustering and prior distributions in hierarchical models.
format Preprint
id arxiv_https___arxiv_org_abs_2605_01603
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle dirichletprocess: An R Package for Fitting Complex Bayesian Nonparametric Models
Ross, Gordon J.
Markwick, Dean
Tiwari, Priyanshu
Computation
The dirichletprocess package provides software for creating flexible Dirichlet process objects. Users can perform nonparametric Bayesian analysis using Dirichlet processes without the need to program their own inference algorithms. Instead, the user can utilise our pre-built models or specify their own models whilst allowing the dirichletprocess package to handle the Markov chain Monte Carlo sampling. Our Dirichlet process objects can act as building blocks for a variety of statistical models including: density estimation, clustering and prior distributions in hierarchical models.
title dirichletprocess: An R Package for Fitting Complex Bayesian Nonparametric Models
topic Computation
url https://arxiv.org/abs/2605.01603