Convergence rate and concentration inequalities for Gibbs sampling in high dimension
Statistics Theory
2014-10-17 v1 Statistics Theory
Abstract
The objective of this paper is to study the Gibbs sampling for computing the mean of observable in very high dimension - a powerful Markov chain Monte Carlo method. Under the Dobrushin's uniqueness condition, we establish some explicit and sharp estimate of the exponential convergence rate and prove some Gaussian concentration inequalities for the empirical mean.
Keywords
Cite
@article{arxiv.1410.4329,
title = {Convergence rate and concentration inequalities for Gibbs sampling in high dimension},
author = {Neng-Yi Wang and Liming Wu},
journal= {arXiv preprint arXiv:1410.4329},
year = {2014}
}
Comments
Published in at http://dx.doi.org/10.3150/13-BEJ537 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)