Communications in Mathematical Sciences

Volume 15 (2017)

Number 7

Gaussian approximations of small noise diffusions in Kullback–Leibler divergence

Pages: 2087 – 2097

(Fast Communication)

DOI: http://dx.doi.org/10.4310/CMS.2017.v15.n7.a13

Authors

Daniel Sanz-Alonso (Mathematics Institute, University of Warwick, Coventry, United Kingdom)

Andrew M. Stuart (Mathematics Institute, University of Warwick, Coventry, United Kingdom)

Abstract

We study Gaussian approximations to the distribution of a diffusion. The approximations are easy to compute: they are defined by two simple ordinary differential equations for the mean and the covariance. Time correlations can also be computed via solution of a linear stochastic differential equation. We show, using the Kullback–Leibler divergence, that the approximations are accurate in the small noise regime. An analogous discrete time setting is also studied. The results provide both theoretical support for the use of Gaussian processes in the approximation of diffusions, and methodological guidance in the construction of Gaussian approximations in applications.

Keywords

Gaussian approximations, diffusion processes, small noise, Kullback–Leibler divergence

2010 Mathematics Subject Classification

28C20, 60G15, 60H10, 65L05

Full Text (PDF format)

Daniel Sanz-Alonso is supported by EPSRC as part of the MASDOC DTC at the University of Warwick with grant No. EP/H023364/1. The work of Andrew M. Stuart is supported by DARPA, EPSRC and ONR.

Paper received on 19 May 2016.

Paper accepted on 30 September 3016.