BayesMix: Bayesian Mixture Models in C++

TL;DR

BayesMix is a C++ library designed for efficient Bayesian mixture model inference using MCMC, emphasizing extensibility and speed, making it accessible for practitioners and outperforming existing software in runtime.

Contribution

The paper introduces BayesMix, a highly extensible and computationally efficient C++ library for Bayesian mixture models with minimal coding for customization.

Findings

Code runtimes are 2 to 25 times faster than competitors.

Library is highly extensible for various mixture models.

Efficient performance across data dimensions 1 to 10.

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. Our library is publicly available on Github at https://github.com/bayesmix-dev/bayesmix/.