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Abstract:
Based on a simple convexity lemma, we develop bounds for
different types of Bayesian prediction errors for regression with
Gaussian processes. The basic bounds are formulated for a fixed
training set. Simpler expressions are obtained for sampling from an
input distribution which equals the weight function of the
covariance kernel, yielding asymptotically tight results. The
results are compared with numerical experiments.
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