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
Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program...
In many real-world applications, we are interested in approximating functions that are analytically ...
In this study, the parameters of a stochastic-dynamical model of sea surface winds are estimated fro...
A Bayesian procedure for the retrieval of wind vectors over the ocean using satellite borne scattero...
This report outlines the derivation and application of a non-zero mean, polynomial-exponential covar...
In many problems in spatial statistics it is necessary to infer a global problem solution by combini...
This technical report builds on previous reports to derive the likelihood and its derivatives for a ...
This is the final version of the article. Available from the arXiv.org via the link in this record.W...
We study online approximations to Gaussian process models for spatially distributed systems. We appl...
International audienceA multisite stochastic generator for wind speed is proposed. It aims at simula...
The resolution of the products of numerical weather prediction is limited by the resolution of numer...
In this study, the parameters of a stochastic–dynamical model of sea surface winds are estimated fro...
Big Data refers to the complexity, high-dimensionality, and high volume of information which are com...
This report seeks to make concrete some of the ideas we have been discussing about sensible priors f...
In many problems in spatial statistics it is necessary to infer a global problem solution by combini...
Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program...
In many real-world applications, we are interested in approximating functions that are analytically ...
In this study, the parameters of a stochastic-dynamical model of sea surface winds are estimated fro...
A Bayesian procedure for the retrieval of wind vectors over the ocean using satellite borne scattero...
This report outlines the derivation and application of a non-zero mean, polynomial-exponential covar...
In many problems in spatial statistics it is necessary to infer a global problem solution by combini...
This technical report builds on previous reports to derive the likelihood and its derivatives for a ...
This is the final version of the article. Available from the arXiv.org via the link in this record.W...
We study online approximations to Gaussian process models for spatially distributed systems. We appl...
International audienceA multisite stochastic generator for wind speed is proposed. It aims at simula...
The resolution of the products of numerical weather prediction is limited by the resolution of numer...
In this study, the parameters of a stochastic–dynamical model of sea surface winds are estimated fro...
Big Data refers to the complexity, high-dimensionality, and high volume of information which are com...
This report seeks to make concrete some of the ideas we have been discussing about sensible priors f...
In many problems in spatial statistics it is necessary to infer a global problem solution by combini...
Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program...
In many real-world applications, we are interested in approximating functions that are analytically ...
In this study, the parameters of a stochastic-dynamical model of sea surface winds are estimated fro...