Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians
Abstract
The mixing time of a Markov chain is the minimum time t necessary for the total variation distance between the distribution of the Markov chain’s current state X_t and its stationary distribution to fall below some ε> 0. In this paper, we present lower bounds for the mixing time of the Gibbs sampler over Gaussian mixture models with Dirichlet priors.
Cite
Text
Tosh and Dasgupta. "Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians." International Conference on Machine Learning, 2014.Markdown
[Tosh and Dasgupta. "Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians." International Conference on Machine Learning, 2014.](https://mlanthology.org/icml/2014/tosh2014icml-lower/)BibTeX
@inproceedings{tosh2014icml-lower,
title = {{Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians}},
author = {Tosh, Christopher and Dasgupta, Sanjoy},
booktitle = {International Conference on Machine Learning},
year = {2014},
pages = {1467-1475},
volume = {32},
url = {https://mlanthology.org/icml/2014/tosh2014icml-lower/}
}