The following Matlab project contains the source code and Matlab examples used for multivariate gaussian mixture model optimization by cross entropy. Fit a multivariate gaussian mixture by a cross-entropy method.

The following Matlab project contains the source code and Matlab examples used for 1d infinite gaussian mixture model. This is a little script which was designed for educational purposes.

The following Matlab project contains the source code and Matlab examples used for 2d infinite gaussian mixture model. This is a little script which was designed for educational purposes.

The following Matlab project contains the source code and Matlab examples used for gaussian mixture model. Gaussian Mixture model is used in many fields to model a training set of data owing to certain similarities among them.

The following Matlab project contains the source code and Matlab examples used for 3d visualization of gmm learning via the em algorithm. This is a 3D visualization of how the Expectation Maximization algorithm learns a Gaussian Mixture Model for 3-dimensional data.

The following Matlab project contains the source code and Matlab examples used for gaussian mixture model (gmm) gaussian mixture regression (gmr). GMM-GMR is a set of Matlab functions to train a Gaussian Mixture Model (GMM) and retrieve generalized data through Gaussian Mixture Regression (GMR).

The following Matlab project contains the source code and Matlab examples used for gaussian mixture modeling gui (gmm demo). The Expectation-Maximization algorithm (EM) is widely used to find the parameters of a mixture of Gaussian probability density functions (pdfs) or briefly Gaussian components that fits the sample measurement vectors in maximum likelihood sense [1].

The following Matlab project contains the source code and Matlab examples used for gaussian mixture model. The GMM returns the cluster centroid and cluster variances for a family of points if the number of clusters are predefined.

The following Matlab project contains the source code and Matlab examples used for em algorithm for gaussian mixture model with background noise. This is the standard EM algorithm for GMMs, presented in Bishop's book "Pattern Recognition and Machine Learning", Chapter 9, with one small exception, the addition of a uniform distribution to the mixture to pick up background noise/speckle; data points which one would not want to associate with any cluster.

The following Matlab project contains the source code and Matlab examples used for ziheng gmm. The code implements the Gaussian mixture model. It assumes that the features are independent. Specifically, GMMtrain.m is used to learn the GMM model and GMMpredict.m is used to predict the cluster labels.

The following Matlab project contains the source code and Matlab examples used for gmm hmrf. In this project, we first study the Gaussian-based hidden Markov random field (HMRF) model and its expectation-maximization (EM) algorithm.