[PyMCon Web Series 03a] Introduction to Hilbert Space GPs (HSGPs) in PyMC (Mar 15, 2023) (Bill Engels)

Events PyMCon Web Series
25

@bwengals I just saw the video of your talk and your HSGP approach looks very promising to handle (my) common performance problems with GPs! :slightly_smiling_face:

However, there were just two questions coming into my mind:

  1. Is HSGP also working with WrapedInput kernels? I’m frequently using WrapedInput to model GPs on non-euclidean domains, e.g. data on a 2-D sphere.

  2. I’m not an expert on Hilbert space methods, but if I understood your approach correctly, HSGP is basically approximating the GP via a Fourier series with random coefficients. Then, it should be quite natural to extend this method to use any kind of orthonormal basis functions for approximation, e.g. orthogonal polynomials, wavelets, etc. - shouldn’t it? Intuitively, I would have maybe chosen Hermite polynomials to approximate GPs on an euclidean domain, to avoid the artificial periodicity that seems to be currently introduced if you predict values outside [-L, L].