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This paper presents a unified treatment of Gaussian process models that extends to data from the exponential dispersion family and to survival 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...
In a variety of disciplines such as social sciences, psychology, medicine and economics, the recorded data are considered to be noisy measurements of latent variables connected by some causal stru...
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...
We use rescaled Gaussian processes as prior models for functional parameters in nonparametric statistical models. We show how the rate of contraction of the posterior distributions depends on the scal...
Upper bounds for rates of convergence of posterior distributions associated to Gaussian process priors are obtained by van der Vaart and van Zanten in [van der Vaart and van Zanten, Rates of contracti...
The measured properties of stellar oscillations can provide powerful constraints on the internal structure and composition of stars. To begin this process, oscillation frequencies must be extracted ...
Consider binary observations whose response probability is an unknown smooth function of a set of covariates. Suppose that a prior on the response probability function is induced by a Gaussian proce...

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