PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Gaussian Process Approximations of Stochastic Differential Equations
Cedric Archambeau, Dan Cornford, Manfred Opper and John Shawe-Taylor
Journal of Machine Learning Research Workshop and Conference Proceedings Volume 1, pp. 1-16, 2007.

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Abstract

Stochastic differential equations arise naturally in a range of contexts, from financial to environmental modeling. Current solution methods are limited in their representation of the posterior process in the presence of data. In this work, we present a novel Gaussian process approximation to the posterior measure over paths for a general class of stochastic differential equations in the presence of observations. The method is applied to two simple problems: the Ornstein-Uhlenbeck process, of which the exact solution is known and can be compared to, and the double-well system, for which standard approaches such as the ensemble Kalman smoother fail to provide a satisfactory result. Experiments show that our variational approximation is viable and that the results are very promising as the variational approximate solution outperforms standard Gaussian process regression for non-Gaussian Markov processesExperiments show that our variational approximation is viable and that the results are very promising as the variational approximation outperforms standard Gaussian process regression for non-Gaussian Markov processes.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3127
Deposited By:Cedric Archambeau
Deposited On:21 December 2007

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