Bayesian Sampling for Stick-Breaking Mixtures

This is a bare-bones implementation of sampling algorithms for a variety of Bayesian stick-breaking (marginally DP) mixture models, including particle learning and Gibbs sampling for static DP mixtures, particle learning for dynamic BAR stick-breaking, and DP mixture regression. The software is designed to be easy to customize to suit different situations and for experimentation with stick-breaking models. Since particles are repeatedly copied, it is not an especially efficient implementation.


Reference manual

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0.6 by Matt Taddy, 5 years ago

Browse source code at

Authors: Matt Taddy <[email protected]>

Documentation:   PDF Manual  

Task views: Bayesian Inference, Cluster Analysis & Finite Mixture Models

GPL (>= 2) license

Depends on mvtnorm

See at CRAN