BayesMix: Bayesian Mixture Models in C++

We describe BayesMix, a C++ library for MCMC posterior simulation for general Bayesian mixture models. The goal of BayesMix is to provide a self-contained ecosystem to perform inference for mixture models to computer scientists, statisticians and practitioners. The key idea of this library is exten...

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Main Authors: Mario Beraha, Bruno Guindani, Matteo Gianella, Alessandra Guglielmi
Format: Article
Language:English
Published: Foundation for Open Access Statistics 2025-03-01
Series:Journal of Statistical Software
Online Access:https://www.jstatsoft.org/index.php/jss/article/view/4743
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author Mario Beraha
Bruno Guindani
Matteo Gianella
Alessandra Guglielmi
author_facet Mario Beraha
Bruno Guindani
Matteo Gianella
Alessandra Guglielmi
author_sort Mario Beraha
collection DOAJ
description We describe BayesMix, a C++ library for MCMC posterior simulation for general Bayesian mixture models. The goal of BayesMix is to provide a self-contained ecosystem to perform inference for mixture models to computer scientists, statisticians and practitioners. The key idea of this library is extensibility, as we wish the users to easily adapt our software to their specific Bayesian mixture models. In addition to the several models and MCMC algorithms for posterior inference included in the library, new users with little familiarity on mixture models and the related MCMC algorithms can extend our library with minimal coding effort. Our library is computationally very efficient when compared to competitor software. Examples show that the typical code runtimes are from two to 25 times faster than competitors for data dimension from one to ten. We also provide Python (bayesmixpy) and R (bayesmixr) interfaces. Our library is publicly available on GitHub at https://github.com/bayesmix-dev/bayesmix/.
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institution Kabale University
issn 1548-7660
language English
publishDate 2025-03-01
publisher Foundation for Open Access Statistics
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series Journal of Statistical Software
spelling doaj-art-7c8301599dae4063ad31bf07da18550e2025-08-20T03:32:16ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602025-03-01112110.18637/jss.v112.i09BayesMix: Bayesian Mixture Models in C++Mario Beraha0https://orcid.org/0000-0002-3495-414XBruno Guindani1https://orcid.org/0000-0002-1710-3466Matteo Gianella2https://orcid.org/0000-0002-3165-3579Alessandra Guglielmi3https://orcid.org/0000-0001-7005-7588University of Milano-BicoccaPolitecnico di MilanoPolitecnico di MilanoPolitecnico di Milano We describe BayesMix, a C++ library for MCMC posterior simulation for general Bayesian mixture models. The goal of BayesMix is to provide a self-contained ecosystem to perform inference for mixture models to computer scientists, statisticians and practitioners. The key idea of this library is extensibility, as we wish the users to easily adapt our software to their specific Bayesian mixture models. In addition to the several models and MCMC algorithms for posterior inference included in the library, new users with little familiarity on mixture models and the related MCMC algorithms can extend our library with minimal coding effort. Our library is computationally very efficient when compared to competitor software. Examples show that the typical code runtimes are from two to 25 times faster than competitors for data dimension from one to ten. We also provide Python (bayesmixpy) and R (bayesmixr) interfaces. Our library is publicly available on GitHub at https://github.com/bayesmix-dev/bayesmix/. https://www.jstatsoft.org/index.php/jss/article/view/4743
spellingShingle Mario Beraha
Bruno Guindani
Matteo Gianella
Alessandra Guglielmi
BayesMix: Bayesian Mixture Models in C++
Journal of Statistical Software
title BayesMix: Bayesian Mixture Models in C++
title_full BayesMix: Bayesian Mixture Models in C++
title_fullStr BayesMix: Bayesian Mixture Models in C++
title_full_unstemmed BayesMix: Bayesian Mixture Models in C++
title_short BayesMix: Bayesian Mixture Models in C++
title_sort bayesmix bayesian mixture models in c
url https://www.jstatsoft.org/index.php/jss/article/view/4743
work_keys_str_mv AT marioberaha bayesmixbayesianmixturemodelsinc
AT brunoguindani bayesmixbayesianmixturemodelsinc
AT matteogianella bayesmixbayesianmixturemodelsinc
AT alessandraguglielmi bayesmixbayesianmixturemodelsinc