Scalable Gaussian-process regression and variable selection using Vecchia approximations. (arXiv:2202.12981v3 [stat.ME] UPDATED)

Gaussian process (GP) regression is a flexible, nonparametric approach to
regression that naturally quantifies uncertainty. In many applications, the
number of responses and covariates are both large, and a goal is to select
covariates that are related to the response. For this setting, we propose a
novel, scalable algorithm, coined VGPR, which optimizes a penalized GP
log-likelihood based on the Vecchia GP approximation, an ordered conditional
approximation from spatial statistics that implies a sparse Cholesky factor of
the precision matrix. …

arxiv gaussian-process process regression scalable