Editing Mixture model (section)

== External links ==
*{{Cite book |first1=Frank |last1=Nielsen |title=2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |chapter=K-MLE: A fast algorithm for learning statistical mixture models |date=23 March 2012 |arxiv=1203.5181 |doi=10.1109/ICASSP.2012.6288022 |pages=869–872 |isbn=978-1-4673-0046-9 |bibcode=2012arXiv1203.5181N |s2cid=935615 }}
* The [http://wiki.stat.ucla.edu/socr/index.php/SOCR_EduMaterials_Activities_2D_PointSegmentation_EM_Mixture SOCR demonstrations of EM and Mixture Modeling]
*[http://www.csse.monash.edu.au/~dld/mixturemodel.html Mixture modelling page] (and the [http://www.csse.monash.edu.au/~dld/Snob.html Snob] program for [[Minimum Message Length]] ([[Minimum Message Length|MML]]) applied to finite mixture models), maintained by D.L. Dowe.
*[http://www.pymix.org PyMix] – Python Mixture Package, algorithms and data structures for a broad variety of mixture model based data mining applications in Python
*[http://scikit-learn.org/stable/modules/mixture.html sklearn.mixture] – A module from the [[scikit-learn]] Python library for learning Gaussian Mixture Models (and sampling from them), previously packaged with [[SciPy]] and now packaged as a [https://scikits.appspot.com/ SciKit]
*[http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=18785&objectType=FILE GMM.m] Matlab code for GMM Implementation
*[http://stat.duke.edu/gpustatsci/software.html GPUmix] C++ implementation of Bayesian Mixture Models using EM and MCMC with 100x speed acceleration using GPGPU.
*[https://www.cs.ru.nl/~ali/index_files/EM.m] Matlab code for GMM Implementation using EM algorithm
*[https://vincentfpgarcia.github.com/jMEF/] jMEF: A Java open source library for learning and processing mixtures of exponential families (using duality with Bregman divergences). Includes a Matlab wrapper.
* Very Fast and clean C implementation of the [https://github.com/juandavm/em4gmm Expectation Maximization] (EM) algorithm for estimating [https://github.com/juandavm/em4gmm Gaussian Mixture Models] (GMMs).  
* [https://cran.r-project.org/web/packages/mclust/index.html mclust] is an R package for mixture modeling.
* [https://github.com/thaines/helit/tree/master/dpgmm dpgmm] Pure Python Dirichlet process Gaussian mixture model implementation (variational).
* [https://mpatacchiola.github.io/blog/2020/07/31/gaussian-mixture-models.html Gaussian Mixture Models] Blog post on Gaussian Mixture Models trained via Expectation Maximization, with an implementation in Python.

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[[Category:Cluster analysis]]
[[Category:Latent variable models]]
[[Category:Probabilistic models]]