Gmix

Gmix is an open-source software framework for probabilistic mixture modeling, focused on Gaussian mixture models and their extensions. It provides algorithms for parameter estimation, model selection, and scalable inference.

The project aims to support researchers and practitioners with a modular architecture that can accommodate various distributions (Gaussian, Student-t, Laplace) and nonparametric mixtures, enabling clustering, density estimation, and anomaly detection.

Features include multiple inference methods (EM, variational inference, Monte Carlo), model evaluation utilities, data preprocessing, and visualization tools. It supports integration with common data formats and has Python bindings while core computations are implemented in C++ for performance.

Technology and performance: optimized linear algebra routines, optional GPU acceleration, and compatibility with CPU parallelism. The design emphasizes reproducibility, with deterministic seeds and logging for experiments.

History and usage: Gmix originated in academic research in the mid-2010s and has since been adopted in data science workflows, genomics, finance, and speech processing. It is distributed under an open-source license and welcomes community contributions.

See also Gaussian mixture model, expectation-maximization algorithm, variational inference, Dirichlet process mixture models.