Year: 2025
Type: article
Abstract: 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/.
Source: Journal of Statistical Software
Institutions: University of Milano-Bicocca, Politecnico di Milano
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: European Commission
Awards: G434337789, G7670237908
OpenAlex ID: W4408970912