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.
|Author||Matt Taddy <email@example.com>|
|Date of publication||2016-02-07 09:18:19|
|Maintainer||Matt Taddy <firstname.lastname@example.org>|
|License||GPL (>= 2)|