Optimal Experimental Design for Heteroscedastic Gaussian Process Emulators (2009)
Boukouvalas, Alexis; Aston University; Boukouva@aston.ac.uk, Cornford, Dan; Aston University; D.Cornford@aston.ac.uk
A wide range of real-world applications are increasingly using stochastic, or random output, simulators to assist in decision and policy making. As such models become increasingly complex,...
Variational Inference in Reduced Order Dynamical Models (2009)
Cornford, Dan; NCRG, Aston University; D.cornford@aston.ac.uk, Shen, Yuan; NCRG, Aston University; Y.shen2@aston.ac.uk, Opper, Manfred; Artificial Intelligence Group, TU Berlin; Opperm@cs.tu-berlin.de
Dynamical systems arise across a range of application domains, from systems biology, to weather forecasting. To study such systems it is necessary to build models to represent the important processes...
Dan Cornford, Lehel Csató, David J. Evans
analysis of the scatterometer wind retrieval inverse problem:
Shen, Yuan, Archambeau, Cedric, Cornford, Dan, Opper, Manfred, Shawe-Taylor, John, Barillec, Remi
In recent years we have developed a novel variational method for partially observed systems governed by stochastic differential equations. In this paper we provide a comparison of the Variational...
inverse problems: some new approaches (2008)
Dan Cornford, Lehel Csató, David J. Evans
Proofs subject to correction. Not to be reproduced without permission. Confidential until read to the Society. Contributions to the discussion must not exceed 400 words. Contributions longer than 400...
Sparse, Sequential Bayesian Geostatistics (2008)
spatial statistics, space-time modelling and data assimilation. Lehel Csato is a post-doc in the same group working on an EPSRC grant (GR/R61857/01) looking at applying sparse sequential Gaussian...
Variational Inference in Stochastic Dynamic Environmental Models (2008)
Dan Cornford, John Shawe-taylor, Ian Roulstone, Peter Clark
The improvements in computational power that are anticipated over the next decade will enable the development of models that permit the study of emergent behaviour of complex interacting systems,...
Bayesian Inference for Wind Field Retrieval Abstract (2008)
In many problems in spatial statistics it is necessary to infer a global problem solution by combining local models. A principled approach to this problem is to develop a global probabilistic model...
Cédric Archambeau, 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...
Shen, Yuan, Archambeau, Cedric, Cornford, Dan, Opper, Manfred
In this paper, we develop a set of novel Markov chain Monte Carlo algorithms for Bayesian inference in partially observed non-linear diffusion processes. The Markov chain Monte Carlo algorithms we...
Variational Inference for Diffusion Processes (2007)
Archambeau, Cedric, Opper, Manfred, Shen, Yuan, Cornford, Dan, Shawe-Taylor, John
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...
Geographic Information Systems (2007)
We live in a world that is increasingly dominated by Information Technology, where databases are continually being created and expanding. However data 6 = information, so as computer scientists one...
1 Improved Multi-beam Neural Network Scatterometer Forward Models (2007)
Dan Cornford, Ian T. Nabney, Guillaume Ramage
Current methods for retrieving near surface winds from scatterometer observations over the ocean surface require a foward sensor model which maps the wind vector to the measured backscatter. This...
Shen, Yuan, Archambeau, Cedric, Cornford, Dan, Opper, Manfred, Shawe-Taylor, John, Barillec, Remi
In recent work we have developed a novel variational inference method for partially observed systems governed by stochastic differential equations. In this paper we provide a comparison of the...
Shen, Yuan, Archambeau, Cedric, Cornford, Dan, Opper, Manfred, Shawe-Taylor, John, Barillec, Remi
In recent work we have developed a novel variational inference method for partially observed systems governed by stochastic differential equations. In this paper we provide a comparison of the...
Gaussian Process Approximations of Stochastic Differential Equations (2007)
Archambeau, Cedric, Cornford, Dan, Opper, Manfred, Shawe-Taylor, John
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...
Gaussian Process Approximations of Stochastic Differential Equation (2007)
Archambeau, Cedric, Cornford, Dan, Opper, Manfred, Shawe-Taylor, John
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...
Variational Inference for Diffusion Processes (2007)
Archambeau, Cedric, Opper, Manfred, Shen, Yuan, Cornford, Dan, Shawe-Taylor, John
Diffusion processes are a family of continuous-time continuous-state stochastic processes that are in general only partly observed. The joint estimation of the forcing parameters and the system noise...
Gaussian process approximations of stochastic differential equations (2007)
Cédric Archambeau, Dan Cornford, D. Lawrence, Anton Schwaighofer, Joaquin Quiñonero C
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...
Gaussian Process Approximations of Stochastic Differential Equations (2006)
Archambeau, Cedric, Cornford, Dan, Opper, Manfred, Shawe-Taylor, John
Stochastic differential equations arise naturally in a range of contexts, from financial to environmental modelling. Current solution methods are based on a range of strong and weak approximation...
We live in a world that is increasingly dominated by Information Technology, where databases are continually being created and expanding. However data � = information, so as computer scientists one...
We live in a world that is increasingly dominated by Information Technology, where databases are continually being created and expanding. However data � = information, so as computer scientists one...
Bayesian Analysis of the Scatterometer Wind Retrieval inverse Problem: Some new Approaches (2004)
Cornford, Dan, Csato, Lehel, Evans, David J, Opper, Manfred
The retrieval of wind vectors from satellite observed radar backscatter can be seen as a non-linear inverse problem. A common approach to solving inverse problems is the Bayesian framework: to infer...
Bayesian analysis of the scatterometer wind retrieval inverse problem: some new approaches (2004)
Dan Cornford, Lehel Csató, David J. Evans
some new approaches
Graphics OUTPUT Description Pattern Recognition Data Processing (2004)
This course is meant as an introduction to computer graphics, which covers a large body of work. The intention is to give a solid grounding in basic 2D computer graphics and introduce the concepts...
Improved neural network scatterometer forward models (2001)
Dan Cornford, Ian T. Nabney, Guillaume Ramage
Abstract. Current retrieval methods for wind vectors from scatterometer observations over the ocean surface requires a sensor model relating the measured backscatter to the wind vector. The...
Online learning of wind-field models (2001)
Abstract. We study online approximations to Gaussian process models for spatially distributed systems. We apply our method to the prediction of wind fields over the ocean surface from scatterometer...
Bayesian inference for wind field retrieval (2000)
In many problems in spatial statistics it is necessary to infer a global problem solution by combining local models. A principled approach to this problem is to develop a global probabilistic model...
Adding Constrained Discontinuities to Gaussian Process Models of Wind Fields (1999)
Accepted NIPS98 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...
Bayesian analysis of the scatterometer wind retrieval inverse problem: some new approaches
Dan Cornford, Lehel Csató, David J. Evans, Manfred Opper
The retrieval of wind vectors from satellite scatterometer observations is a non-linear inverse problem. A common approach to solving inverse problems is to adopt a Bayesian framework and to infer...