Joaquin Quiñonero C, Agathe Girard, Jan Larsen, Carl Edward Rasmussen
The object of Bayesian modelling is the predictive distribution, which in a forecasting scenario enables evaluation of forecasted values and their uncertainties. In this paper we focus on reliably...
Joaquin Quiñonero C, Agathe Girard, Jan Larsen, Carl Edward Rasmussen
The object of Bayesian modelling is the predictive distribution, which in a forecasting scenario enables evaluation of forecasted values and their uncertainties. In this paper we focus on reliably...
Agathe Girard, Joaquin Quiñonero Candela, Roderick Murray-smith, Carl Edward Rasmussen
We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model.-step ahead forecasting of a discrete-time non-linear dynamic system can...
Agathe Girard, Joaquin Quiñonero Candela, Roderick Murray-smith, Carl Edward Rasmussen
We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model.�-step ahead forecasting of a discrete-time non-linear dynamic system...
Agathe Girard, Carl Edward Rasmussen, Roderick Murray-smith
We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. k-step ahead forecasting of a discrete-time nonlinear dynamic system...
Agathe Girard, Roderick Murray-smith
Learning with uncertain inputs is well-known to be a difficult task. In order to achieve this analytically using a Gaussian Process prior model, we expand the original process around the input mean...
Agathe Girard, Carl Edward Rasmussen, Math Modelling
The object of Bayesian modelling is the predictive distribution, which in a forecasting scenario enables evaluation of forecasted values and their uncertainties. In this paper we focus on reliably...
dynamic systems-- A Gaussian Process (2007)
Agathe Girard, Carl Edward Rasmussen, Roderick Murray-smith
Multiple-step ahead prediction for non linear
Agathe Girard, Joaquin Quiñonero Candela, Roderick Murray-smith, Carl Edward Rasmussen
We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. �-step ahead forecasting of a discrete-time non-linear dynamic system...
Approximate methods for propagation of uncertainty with Gaussian process models / (2004)
Ph. D. thesis submitted to the Department of Computing Science, University of Glasgow, 2004.
Agathe Girard, C Agathe Girard
This thesis presents extensions of the Gaussian Process (GP) model, based on approximate methods allowing the model to deal with input uncertainty. Zero-mean GPs with Gaussian covariance function are...
Adaptive, cautious, predictive control with Gaussian process priors (2003)
Roderick Murray-Smith, Daniel Sbarbaro, Carl Edward Rasmussen, Agathe Girard
Nonparametric Gaussian Process models, a Bayesian statistics approach, are used to implement a nonlinear adaptive control law. Predictions, including propagation of the state uncertainty are made...
Gaussian process priors with uncertain inputs: Multiple-step ahead prediction (2003)
Agathe Girard, Carl Edward Rasmussen, Mathematical Modelling, Roderick Murray-smith
We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. k-step ahead forecasting of a discrete-time non-linear dynamic system...
Agathe Girard, Carl Edward Rasmussen
We consider in this report non-linear models that map an input D-dimensional column vector x into a single dimensional output f(x). The non-linear mapping f() is implemented by means of a Gaussian...
Title ASSESSING THE COMPLEXITY OF MULTI-DIMENSIONAL MIS- FIT FUNCTIONS (1999)
Old Quarry Road, Agathe Girard, Andrew Curtis, Schlumberger Cambridge, Author(s Agathe Girard, Andrew Curtis
This report compares two methods to assess the complexity of nonlinear inverse problems. Each method attempts to estimate the number of minima in the data misfit function. The first method uses...