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Convergence analysis of non-stationary and deep Gaussian process regression

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We are interested in the task of estimating an unknown function from data, given as a set of point evaluations. In this context, Gaussian process regression is often used as a Bayesian inference procedure, and we are interested in the convergence as the number of data points goes to infinity. Using results from scattered data approximation, we provide a convergence analysis of the method applied to a given, unknown function of interest. We are particularly interested in the case of non-stationary covariance kernels, and the extension of the results to deep Gaussian processes.

This is joint work with Conor Osborne (University of Edinburgh).

This talk is part of the Applied and Computational Analysis series.

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