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/.

中文翻译：

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BayesMix：C++ 中的贝叶斯混合模型

我们描述了 BayesMix，一个用于 MCMC 后验模拟的 C++ 库，用于一般贝叶斯混合模型。BayesMix 的目标是为计算机科学家、统计学家和从业者提供一个独立的生态系统来执行混合模型的推理。这个库的关键思想是可扩展性，因为我们希望用户能够轻松地使我们的软件适应他们特定的贝叶斯混合模型。除了库中包含的用于后验推理的几个模型和 MCMC 算法之外，对混合模型和相关 MCMC 算法不太熟悉的新用户可以用最少的编码工作来扩展我们的库。与竞争对手的软件相比，我们的库在计算上非常高效。示例表明，对于从 1 到 10 的数据维度，典型的代码运行时间比竞争对手快 2 到 25 倍。