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Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
We consider the problem of detecting the periodic part of a function given the observations of some input/output tuples (xi,yi). As they are known for being powerful tools for dealing with such data, ...
For many expensive deterministic computer simulators, the outputs do not have replication error and the desired metamodel (or emulator) is an interpolator of the observed data. Realizations of Gauss...
Our primary goal is to obtain a smoothed summary estimate of the magnetic eld generated in and near to the Milky Way by using Faraday rotation measures (RMs) Each RM in our data set provides an inte...

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