BayesMix: Bayesian Mixture Models in C++

05/17/2022

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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/.

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BayesMix: Bayesian Mixture Models in C++

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Kernel learning approaches for summarising and combining posterior similarity matrices

Posterior Predictive Null Checks

Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models

JAGS, NIMBLE, Stan: a detailed comparison among Bayesian MCMC software

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BayesMix: Bayesian Mixture Models in C++

Related Research

MCMC computations for Bayesian mixture models using repulsive point processes

Bayesian Finite Mixtures of Ising Models

Kernel learning approaches for summarising and combining posterior similarity matrices

Posterior Predictive Null Checks

Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models

JAGS, NIMBLE, Stan: a detailed comparison among Bayesian MCMC software

The Shapley Value of coalition of variables provides better explanations