A Hybrid Sampler for Poisson-Kingman Mixture Models
Abstract
This paper concerns the introduction of a new Markov Chain Monte Carlo scheme for posterior sampling in Bayesian nonparametric mixture models with priors that belong to the general Poisson-Kingman class. We present a novel and compact way of representing the infinite dimensional component of the model such that while explicitly representing this infinite component it has less memory and storage requirements than previous MCMC schemes. We describe comparative simulation results demonstrating the efficacy of the proposed MCMC algorithm against existing marginal and conditional MCMC samplers.
Cite
Text
Lomeli et al. "A Hybrid Sampler for Poisson-Kingman Mixture Models." Neural Information Processing Systems, 2015.Markdown
[Lomeli et al. "A Hybrid Sampler for Poisson-Kingman Mixture Models." Neural Information Processing Systems, 2015.](https://mlanthology.org/neurips/2015/lomeli2015neurips-hybrid/)BibTeX
@inproceedings{lomeli2015neurips-hybrid,
title = {{A Hybrid Sampler for Poisson-Kingman Mixture Models}},
author = {Lomeli, Maria and Favaro, Stefano and Teh, Yee Whye},
booktitle = {Neural Information Processing Systems},
year = {2015},
pages = {2161-2169},
url = {https://mlanthology.org/neurips/2015/lomeli2015neurips-hybrid/}
}