Computational Solutions for Bayesian Inference in Mixture Models
Computation
2018-12-19 v1
Abstract
This chapter surveys the most standard Monte Carlo methods available for simulating from a posterior distribution associated with a mixture and conducts some experiments about the robustness of the Gibbs sampler in high dimensional Gaussian settings. This is a chapter prepared for the forthcoming 'Handbook of Mixture Analysis'.
Cite
@article{arxiv.1812.07240,
title = {Computational Solutions for Bayesian Inference in Mixture Models},
author = {Gilles Celeux and Kaniav Kamary and Gertraud Malsiner-Walli and Jean-Michel Marin and Christian P. Robert},
journal= {arXiv preprint arXiv:1812.07240},
year = {2018}
}