An accuracy-runtime trade-off comparison of scalable Gaussian process approximations for spatial data

Filippo Rambelli, Fabio Sigrist

Gaussian processes (GPs) are flexible, probabilistic, nonparametric models widely used in fields such as spatial statistics and machine learning. A drawback of Gaussian processes i…