Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models
Abstract
Nonparametric mixture models based on the Dirichlet process are an elegant alternative to finite models when the number of underlying components is unknown, but inference in such models can be slow. Existing attempts to parallelize inference in such models have relied on introducing approximations, which can lead to inaccuracies in the posterior estimate. In this paper, we describe auxiliary variable representations for the Dirichlet process and the hierarchical Dirichlet process that allow us to perform MCMC using the correct equilibrium distribution, in a distributed manner. We show that our approach allows scalable inference without the deterioration in estimate quality that accompanies existing methods.
Cite
Text
Williamson et al. "Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models." International Conference on Machine Learning, 2013.Markdown
[Williamson et al. "Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models." International Conference on Machine Learning, 2013.](https://mlanthology.org/icml/2013/williamson2013icml-parallel/)BibTeX
@inproceedings{williamson2013icml-parallel,
title = {{Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models}},
author = {Williamson, Sinead and Dubey, Avinava and Xing, Eric},
booktitle = {International Conference on Machine Learning},
year = {2013},
pages = {98-106},
volume = {28},
url = {https://mlanthology.org/icml/2013/williamson2013icml-parallel/}
}