Bmix: 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.

Getting started

Package details

AuthorMatt Taddy <>
MaintainerMatt Taddy <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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Bmix documentation built on May 1, 2019, 6:47 p.m.