Dan Cornford

ICML Conference 2012 Conference Paper

Gaussian Process Quantile Regression using Expectation Propagation

• Alexis Boukouvalas
• Remi Louis Barillec
• Dan Cornford

NeurIPS Conference 2007 Conference Paper

Variational Inference for Diffusion Processes

• Cédric Archambeau
• Manfred Opper
• Yuan Shen
• Dan Cornford
• John Shawe-Taylor

Diffusion processes are a family of continuous-time continuous-state stochastic processes that are in general only partially observed. The joint estimation of the forcing parameters and the system noise (volatility) in these dynamical systems is a crucial, but non-trivial task, especially when the system is nonlinear and multi-modal. We propose a variational treatment of diffusion processes, which allows us to estimate these parameters by simple gradient techniques and which is computationally less demanding than most MCMC approaches. Furthermore, our parameter inference scheme does not break down when the time step gets smaller, unlike most current approaches. Finally, we show how a cheap estimate of the posterior over the parameters can be constructed based on the variational free energy.

NeurIPS Conference 1998 Conference Paper

Adding Constrained Discontinuities to Gaussian Process Models of Wind Fields

• Dan Cornford
• Ian Nabney
• Christopher Williams

Gaussian Processes provide good prior models for spatial data, but can be too smooth. In many physical situations there are discontinuities along bounding surfaces, for example fronts in near-surface wind fields. We describe a modelling method for such a constrained discontinuity and demonstrate how to infer the model parameters in wind fields with MCMC sampling.