statistical inference

Efficient and Accurate Inference for Matérn Gaussian Processes on Intervals

1 min read · Mon, Jun 16 2025

Gaussian processes statistical inference

A new study by researchers at KAUST introduces a breakthrough method for statistical modeling with Matérn Gaussian processes, a popular tool in spatial statistics, machine learning, and the natural sciences. While Gaussian processes with stationary Matérn covariance functions (Matérn processes) are valued for their flexibility, their use with large datasets has been severely limited by the high computational cost of standard methods. The team has developed the first generally applicable approach that enables fast, linear-cost inference and prediction for Matérn processes on bounded intervals

Alexandre de Bustamante Simas

Senior Research Scientist, Statistics

stochastic processes statistical inference Partial Differential Equations spatial statistics probability theory