General Bounds on Bayes Errors for Regression with Gaussian Processes

Based on a simple convexity lemma, we develop bounds for differ(cid: 173) ent 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 distri(cid: 173) bution which equals the weight function of the covariance kernel, yielding asymptotically tight results. The results are compared with numerical experiments.

Authors

• Manfred Opper
• Francesco Vivarelli

Keywords

No keywords are indexed for this paper.