Open Access
February 2014 Minimising MCMC variance via diffusion limits, with an application to simulated tempering
Gareth O. Roberts, Jeffrey S. Rosenthal
Ann. Appl. Probab. 24(1): 131-149 (February 2014). DOI: 10.1214/12-AAP918

Abstract

We derive new results comparing the asymptotic variance of diffusions by writing them as appropriate limits of discrete-time birth–death chains which themselves satisfy Peskun orderings. We then apply our results to simulated tempering algorithms to establish which choice of inverse temperatures minimises the asymptotic variance of all functionals and thus leads to the most efficient MCMC algorithm.

Citation

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Gareth O. Roberts. Jeffrey S. Rosenthal. "Minimising MCMC variance via diffusion limits, with an application to simulated tempering." Ann. Appl. Probab. 24 (1) 131 - 149, February 2014. https://doi.org/10.1214/12-AAP918

Information

Published: February 2014
First available in Project Euclid: 9 January 2014

zbMATH: 1298.60078
MathSciNet: MR3161644
Digital Object Identifier: 10.1214/12-AAP918

Subjects:
Primary: 60J22
Secondary: 62F10 , 62M05

Keywords: diffusion limits , Markov chain Monte Carlo , Optimal scaling , simulated tempering

Rights: Copyright © 2014 Institute of Mathematical Statistics

Vol.24 • No. 1 • February 2014
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