Mat\'ern Correlation

Matérn correlation functions are crucial components of Gaussian processes, widely used in machine learning for modeling spatial and temporal data, particularly where uncertainty quantification is vital. Current research focuses on improving the scalability and efficiency of Matérn-based Gaussian process regression, especially in high-dimensional settings, through techniques like leveraging implicit manifold structures and employing convolutional neural networks for efficient non-stationary covariance estimation. These advancements enhance the applicability of Matérn models to large-scale datasets in diverse fields, improving predictive accuracy and uncertainty calibration in applications ranging from climate science to machine learning.