Bayesian Sampling for Stick-breaking Mixtures
Description
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.