An accuracy-runtime trade-off comparison of scalable Gaussian process approximations for spatial data
Researchers have compared various scalable Gaussian process approximations for handling large spatial datasets. Their analysis focused on the trade-off between model accuracy and computational runtime across simulated and real-world data. The study found that Vecchia approximations consistently offered the best balance of accuracy and speed for likelihood evaluation, parameter estimation, and prediction. AI
IMPACT Provides a comparative analysis of computational methods for Gaussian processes, relevant for large-scale spatial data analysis in machine learning.