Asymptotic Analysis of the Random-Walk Metropolis Algorithm on Ridged Densities

Alexandros Beskos; Gareth Roberts; Alexandre Thiery; Natesh Pillai

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arXiv · 1510.02577

Asymptotic Analysis of the Random-Walk Metropolis Algorithm on Ridged Densities

Alexandros Beskos·Gareth Roberts·Alexandre Thiery·Natesh Pillai

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Abstract

In this paper we study the asymptotic behavior of the Random-Walk Metropolis algorithm on probability densities with two different `scales', where most of the probability mass is distributed along certain key directions with the `orthogonal' directions containing relatively less mass. Such class of probability measures arise in various applied contexts including Bayesian inverse problems where the posterior measure concentrates on a sub-manifold when the noise variance goes to zero. When the target measure concentrates on a linear sub-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. 2015-10-09. Asymptotic Analysis of the Random-Walk Metropolis Algorithm on Ridged Densities. https://arxiv.org/abs/1510.02577

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