I am a frequentist by training and got a little confused by Bayesian and engineering terminology while flipping through the posts. Bear with me.<p>Did I understand well that kernel interpolation is what we'd call a kind of non-noisy kernel regression? If it's the case and dimension d of the regressors is large, multivariate non-parametric estimation will have very slow convergence, won't it?
Excellent post. Would you have a recommendation on a resource for learning about reproducing kernel Hilbert spaces? In particular, I'm interested in learning more about how they are applied to data analysis. I've been reading about them in a very theoretical sense, but very few math texts provide "real-world" examples of how they are used.