(nonic) polynomial which at the time posed a significant computational challenge. Subsequent works focused on addressing these problems, but it was not until the advent of the modern computer and the popularisation of Maximum Likelihood (MLE) parameterisation techniques that research really took off. Since that time there has been a vast body of research on the subject spanning areas such as fisheries research, agriculture, botany, economics, medicine, genetics, psychology, palaeontology, electrophoresis, finance, geology and zoology. See also Mixture Mixture density Mixture (probability) Flexible Mixture Model (FMM) Subspace Gaussian mixture model Hierarchical models Graphical model Hierarchical Bayes model Outlier detection RANSAC References Further reading Books on mixture models Application of Gaussian mixture models External links The SOCR demonstrations of EM and Mixture Modeling Mixture modelling page (and the Snob program for Minimum Message Length (MML) applied to finite mixture models), maintained by D.L. Dowe. PyMix – Python Mixture Package, algorithms and data structures for a broad variety of mixture model based data mining applications in Python 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 SciKit GMM.m Matlab code for GMM Implementation GPUmix C++ implementation of Bayesian Mixture Models using EM and MCMC with 100x speed acceleration using GPGPU. Matlab code for GMM Implementation using EM algorithm 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 Expectation Maximization (EM) algorithm for estimating Gaussian Mixture Models (GMMs). mclust is an R package for mixture modeling. dpgmm Pure Python Dirichlet process Gaussian mixture model implementation (variational). Gaussian Mixture Models Blog post on Gaussian Mixture Models trained via Expectation Maximization, with an implementation in Python. Cluster analysis Latent variable models Probabilistic models