MixEst: an estimation toolbox for mixture models . 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.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Sembach, Lena; Burgard, Jan Pablo; Schulz, Volker: A Riemannian Newton trust-region method for fitting Gaussian mixture models (2022)
- Hosseini, Reshad; Sra, Suvrit; Theis, Lucas; Bethge, Matthias: Inference and mixture modeling with the elliptical Gamma distribution (2016)
- Sra, Suvrit; Hosseini, Reshad: Geometric optimization in machine learning (2016)