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Dirichlet Process Gaussian Mixture Model

version (6.29 KB) by Mo Chen
Dirichlet Process Gaussian Mixture Model aka Infinite GMM using Gibbs Sampling


Updated 13 Mar 2016

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This package solves the Dirichlet Process Gaussian Mixture Model (aka Infinite GMM) with Gibbs sampling. This is nonparametric Bayesian treatment for mixture model problems which automatically selects the proper number of the clusters.
I includes the Gaussian component distribution in the package. However, the code is flexible enough for Dirichlet process mixture model of any distribution. User can write your own class for the base distribution then let the underlying Gibbs sampling engine do the inference work.
Please try the demo script in the package.

This package is now a part of the PRML toolbox (

Cite As

Mo Chen (2021). Dirichlet Process Gaussian Mixture Model (, MATLAB Central File Exchange. Retrieved .

Comments and Ratings (10)

Chao Jiang

Very good work. It will be better if the author provides references.

Luo Shaqi

xiao luo

zhaowh zhaowh

Jack Ma

Is there any detail description of the algorithm, just like you did for Variational Bayesian Inference for Gaussian Mixture Model?

David Duan

Yongsheng Li

yuebin wang

Yuanxia Zhang


Is it applicable for 1D data as well?

MATLAB Release Compatibility
Created with R2016a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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