# MixEst: An Estimation Toolbox for Mixture Models

arXiv: https://arxiv.org/abs/1507.06065 · HTML: https://mlsanity.com/paper/1507.06065/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2015.

## Built on by (most cited first, 4 of 4)

- [On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry](https://mlsanity.com/paper/2106.00286/) (2021)
- [A Riemannian Newton Trust-Region Method for Fitting Gaussian Mixture Models](https://mlsanity.com/paper/2104.14957/) (2021)
- [Vector Transport Free Riemannian LBFGS for Optimization on Symmetric Positive Definite Matrix Manifolds](https://mlsanity.com/paper/2108.11019/) (2021)
- [Foundations of Riemannian Geometry for Riemannian Optimization: A Monograph with Detailed Derivations](https://mlsanity.com/paper/2605.02279/) (2026)

## Abstract (arXiv)

Mixture models are powerful statistical models used in many applications ranging from density estimation to clustering and classification. When dealing with mixture models, there are many issues that the experimenter should be aware of and needs to solve. The MixEst toolbox is a powerful and user-friendly package for MATLAB that implements several state-of-the-art approaches to address these problems. Additionally, MixEst gives the possibility of using manifold optimization for fitting the density model, a feature specific to this toolbox. MixEst simplifies using and integration of mixture models in statistical models and applications. For developing mixture models of new densities, the user just needs to provide a few functions for that statistical distribution and the toolbox takes care of all the issues regarding mixture models. MixEst is available at visionlab.ut.ac.ir/mixest and is fully documented and is licensed under GPL.