Translator Disclaimer
September 2015 On the convergence rates of some adaptive Markov chain Monte Carlo algorithms
Yves Atchadé, Yizao Wang
Author Affiliations +
J. Appl. Probab. 52(3): 811-825 (September 2015). DOI: 10.1239/jap/1445543848

Abstract

In this paper we study the mixing time of certain adaptive Markov chain Monte Carlo (MCMC) algorithms. Under some regularity conditions, we show that the convergence rate of importance resampling MCMC algorithms, measured in terms of the total variation distance, is O(n-1). By means of an example, we establish that, in general, this algorithm does not converge at a faster rate. We also study the interacting tempering algorithm, a simplified version of the equi-energy sampler, and establish that its mixing time is of order O(n-1/2).

Citation

Download Citation

Yves Atchadé. Yizao Wang. "On the convergence rates of some adaptive Markov chain Monte Carlo algorithms." J. Appl. Probab. 52 (3) 811 - 825, September 2015. https://doi.org/10.1239/jap/1445543848

Information

Published: September 2015
First available in Project Euclid: 22 October 2015

zbMATH: 1339.65006
MathSciNet: MR3414993
Digital Object Identifier: 10.1239/jap/1445543848

Subjects:
Primary: 65C05 , 65C40
Secondary: 60J05

Keywords: Adaptive Markov chain Monte Carlo , equi-energy sampler , importance resampling algorithm , mixing time , total variation distance

Rights: Copyright © 2015 Applied Probability Trust

JOURNAL ARTICLE
15 PAGES

This article is only available to subscribers.
It is not available for individual sale.
+ SAVE TO MY LIBRARY

SHARE
Vol.52 • No. 3 • September 2015
Back to Top