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October 2018 Asymptotic analysis of the random walk Metropolis algorithm on ridged densities
Alexandros Beskos, Gareth Roberts, Alexandre Thiery, Natesh Pillai
Ann. Appl. Probab. 28(5): 2966-3001 (October 2018). DOI: 10.1214/18-AAP1380


We study the asymptotic behaviour of the Random Walk Metropolis algorithm on “ridged” probability densities where most of the probability mass is distributed along some key directions. Such class of probability measures arise in various applied contexts including for instance Bayesian inverse problems where the posterior measure concentrates on a manifold when the noise variance goes to zero. When the target measure concentrates on a linear manifold, we derive analytically a diffusion limit for the Random Walk Metropolis Markov chain as the scale parameter goes to zero. In contrast to the existing works on scaling limits, our limiting stochastic differential equation does not in general have a constant diffusion coefficient. Our results show that in some cases, the usual practice of adapting the step-size to control the acceptance probability might be sub-optimal as the optimal acceptance probability is zero (in the limit).


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Alexandros Beskos. Gareth Roberts. Alexandre Thiery. Natesh Pillai. "Asymptotic analysis of the random walk Metropolis algorithm on ridged densities." Ann. Appl. Probab. 28 (5) 2966 - 3001, October 2018.


Received: 1 December 2016; Revised: 1 September 2017; Published: October 2018
First available in Project Euclid: 28 August 2018

zbMATH: 06974770
MathSciNet: MR3847978
Digital Object Identifier: 10.1214/18-AAP1380

Primary: 65C05 , 65C40
Secondary: 65C30

Keywords: diffusion limit , generator , Manifold , Random-walk metropolis

Rights: Copyright © 2018 Institute of Mathematical Statistics


Vol.28 • No. 5 • October 2018
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