The Annals of Applied Probability

Optimal scaling for the transient phase of the random walk Metropolis algorithm: The mean-field limit

Benjamin Jourdain, Tony Lelièvre, and Błażej Miasojedow

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Abstract

We consider the random walk Metropolis algorithm on $\mathbb{R}^{n}$ with Gaussian proposals, and when the target probability measure is the $n$-fold product of a one-dimensional law. In the limit $n\to\infty$, it is well known (see [ Ann. Appl. Probab. 7 (1997) 110–120]) that, when the variance of the proposal scales inversely proportional to the dimension $n$ whereas time is accelerated by the factor $n$, a diffusive limit is obtained for each component of the Markov chain if this chain starts at equilibrium. This paper extends this result when the initial distribution is not the target probability measure. Remarking that the interaction between the components of the chain due to the common acceptance/rejection of the proposed moves is of mean-field type, we obtain a propagation of chaos result under the same scaling as in the stationary case. This proves that, in terms of the dimension $n$, the same scaling holds for the transient phase of the Metropolis–Hastings algorithm as near stationarity. The diffusive and mean-field limit of each component is a diffusion process nonlinear in the sense of McKean. This opens the route to new investigations of the optimal choice for the variance of the proposal distribution in order to accelerate convergence to equilibrium (see [Optimal scaling for the transient phase of Metropolis–Hastings algorithms: The longtime behavior Bernoulli (2014) To appear]).

Article information

Source
Ann. Appl. Probab., Volume 25, Number 4 (2015), 2263-2300.

Dates
Received: October 2012
Revised: December 2013
First available in Project Euclid: 21 May 2015

Permanent link to this document
https://projecteuclid.org/euclid.aoap/1432212442

Digital Object Identifier
doi:10.1214/14-AAP1048

Mathematical Reviews number (MathSciNet)
MR3349007

Zentralblatt MATH identifier
1329.60262

Subjects
Primary: 60J22: Computational methods in Markov chains [See also 65C40] 60G09: Exchangeability 60F17: Functional limit theorems; invariance principles 65C05: Monte Carlo methods 65C40: Computational Markov chains

Keywords
Diffusion limits optimal scaling random walk Metropolis propagation of chaos

Citation

Jourdain, Benjamin; Lelièvre, Tony; Miasojedow, Błażej. Optimal scaling for the transient phase of the random walk Metropolis algorithm: The mean-field limit. Ann. Appl. Probab. 25 (2015), no. 4, 2263--2300. doi:10.1214/14-AAP1048. https://projecteuclid.org/euclid.aoap/1432212442


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