Add to ORKGGiven a Gaussian Process with a zero mean and a Squared Exponential (SE) kernel. We are interested in the exact mean and covariance of the predictive distribution of the latent function f and its gradient ∂f/∂x at an uncertain input x ∼ N(μ,Σ). This technical note develops the calculations of these quantities and documents an implementation of these calculations in a Matlab function called gppred_exactmoments_se
Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to in...
The main topic of this thesis are Gaussian processes for machine learning, more precisely the select...
Gaussian processes are a powerful and flexible class of nonparametric models that use covariance fun...
In applications of Gaussian processes where quantification of uncertainty is of primary interest, it...
In this thesis we address the problem of modeling correlated outputs using Gaussian process priors. ...
AbstractLet X(t) be the ergodic Gauss–Markov process with mean zero and covariance function e−|τ|. L...
In applications of Gaussian processes where quantification of uncertainty is of primary in-terest, i...
A new estimator is proposed for the mean function of a Gaussian process with known covariance functi...
In this dissertation Gaussian processes are used to define prior distributions over latent functions...
With the Gaussian Process model, the predictive distribution of the output corresponding to a new gi...
This report non-linear models that map an input D-dimensional column vector x into a single dimensio...
The problem of global estimation of the mean function [theta](·) of a quite arbitrary Gaussian proc...
28 pagesInternational audienceWe consider a real Gaussian process $X$ with unknown smoothness $\ro\i...
This paper first strictly proved that the growth of the second moment of a large class of Gaussian p...
The strong dependence between samples in large spatial data sets is the primary challenge of designi...
Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to in...
The main topic of this thesis are Gaussian processes for machine learning, more precisely the select...
Gaussian processes are a powerful and flexible class of nonparametric models that use covariance fun...
In applications of Gaussian processes where quantification of uncertainty is of primary interest, it...
In this thesis we address the problem of modeling correlated outputs using Gaussian process priors. ...
AbstractLet X(t) be the ergodic Gauss–Markov process with mean zero and covariance function e−|τ|. L...
In applications of Gaussian processes where quantification of uncertainty is of primary in-terest, i...
A new estimator is proposed for the mean function of a Gaussian process with known covariance functi...
In this dissertation Gaussian processes are used to define prior distributions over latent functions...
With the Gaussian Process model, the predictive distribution of the output corresponding to a new gi...
This report non-linear models that map an input D-dimensional column vector x into a single dimensio...
The problem of global estimation of the mean function [theta](·) of a quite arbitrary Gaussian proc...
28 pagesInternational audienceWe consider a real Gaussian process $X$ with unknown smoothness $\ro\i...
This paper first strictly proved that the growth of the second moment of a large class of Gaussian p...
The strong dependence between samples in large spatial data sets is the primary challenge of designi...
Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to in...
The main topic of this thesis are Gaussian processes for machine learning, more precisely the select...
Gaussian processes are a powerful and flexible class of nonparametric models that use covariance fun...