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February 2016 A function space HMC algorithm with second order Langevin diffusion limit
Michela Ottobre, Natesh S. Pillai, Frank J. Pinski, Andrew M. Stuart
Bernoulli 22(1): 60-106 (February 2016). DOI: 10.3150/14-BEJ621


We describe a new MCMC method optimized for the sampling of probability measures on Hilbert space which have a density with respect to a Gaussian; such measures arise in the Bayesian approach to inverse problems, and in conditioned diffusions. Our algorithm is based on two key design principles: (i) algorithms which are well defined in infinite dimensions result in methods which do not suffer from the curse of dimensionality when they are applied to approximations of the infinite dimensional target measure on $\mathbb{R}^{N}$; (ii) nonreversible algorithms can have better mixing properties compared to their reversible counterparts. The method we introduce is based on the hybrid Monte Carlo algorithm, tailored to incorporate these two design principles. The main result of this paper states that the new algorithm, appropriately rescaled, converges weakly to a second order Langevin diffusion on Hilbert space; as a consequence the algorithm explores the approximate target measures on $\mathbb{R}^{N}$ in a number of steps which is independent of $N$. We also present the underlying theory for the limiting nonreversible diffusion on Hilbert space, including characterization of the invariant measure, and we describe numerical simulations demonstrating that the proposed method has favourable mixing properties as an MCMC algorithm.


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Michela Ottobre. Natesh S. Pillai. Frank J. Pinski. Andrew M. Stuart. "A function space HMC algorithm with second order Langevin diffusion limit." Bernoulli 22 (1) 60 - 106, February 2016.


Received: 1 September 2013; Revised: 1 March 2014; Published: February 2016
First available in Project Euclid: 30 September 2015

zbMATH: 1346.60119
MathSciNet: MR3449777
Digital Object Identifier: 10.3150/14-BEJ621

Rights: Copyright © 2016 Bernoulli Society for Mathematical Statistics and Probability


Vol.22 • No. 1 • February 2016
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