Frequentist Consistency of Gaussian Process Regression
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Gaussian Process Regression is a well-known and widely used approach to a problem of non-parametric regression. In the current study we obtain a minimax-optimal rate of convergence of its predictive mean to the true underlying function. We provide results for both random and deterministic designs.
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