An accuracy-runtime trade-off comparison of scalable Gaussian process
approximations for spatial data
An accuracy-runtime trade-off comparison of scalable Gaussian process
approximations for spatial data
Gaussian processes (GPs) are flexible, probabilistic, non-parametric models widely employed in various fields such as spatial statistics, time series analysis, and machine learning. A drawback of Gaussian processes is their computational cost having $\mathcal{O}(N^3)$ time and $\mathcal{O}(N^2)$ memory complexity which makes them prohibitive for large datasets. Numerous approximation techniques have …