Corpus ID: 15105350

Gaussian Process Approximations of Stochastic Differential Equations

@inproceedings{Archambeau2007GaussianPA,
  title={Gaussian Process Approximations of Stochastic Differential Equations},
  author={C. Archambeau and D. Cornford and M. Opper and J. Shawe-Taylor},
  booktitle={Gaussian Processes in Practice},
  year={2007}
}
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… Expand
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