Gaussian Process Approximations of Stochastic Differential Equations

@inproceedings{Archambeau2007GaussianPA,
  title={Gaussian Process Approximations of Stochastic Differential Equations},
  author={C{\'e}dric Archambeau and Dan Cornford and Manfred Opper and John Shawe-Taylor},
  booktitle={Gaussian Processes in Practice},
  year={2007}
}
Some of the most complex models routinely run are numerical weather prediction models. These models are based on a discretisation of a coupled set of partial differential equations (the dynamics) which govern the time evolution of the atmosphere, described in terms of temperature, pressure, velocity, etc, together with parameterisations of physical processes such as radiation and clouds [3]. These dynamical models typically have state vectors with dimension O(106) or more. A key issue in… CONTINUE READING
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