Mixture model projects and source code

Gaussian mixture model in matlab

The following Matlab project contains the source code and Matlab examples used for gaussian mixture model. A Gaussian mixture model means that each data point is drawn (randomly) from one of C classes of data, with probability p_i of being drawn from class i, and each class is distributed as a Gaussian with mean standard deviation mu_i and sigma_i.

Gmm Matlab Code

Generalized method of moments (GMM) is a generic method for estimating parameters in statistical models. Usually it is applied in the context of semiparametric models, where the parameter of interest is finite-dimensional, whereas the full shape of the distribution function of the data may not be known, and therefore the maximum likelihood estimation is not applicable.

Em algorithm for gaussian mixture model with background noise in matlab

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.

Ziheng gmm in matlab

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.

Parameter estimation technique for general datasets in matlab

The following Matlab project contains the source code and Matlab examples used for parameter estimation technique for general datasets. In the paper "An estimation technique for Time Indexed gaussian Mixture Models", we propose a model specification that can be used to describe data with spikes, jumps, mean reversion, geometric brownian motion, you name it.

K Means Clustering Matlab Code

k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.

Subscribe to RSS - Mixture model