The Gaussian process is an indispensable tool for spatial data analysts. The onset of the
“big data” era, however, has lead to the traditional Gaussian process being computationally …

Gaussian process regression underpins countless academic and industrial applications of
machine learning and statistics, with maximum likelihood estimation routinely used to select …

The last decades have seen an unprecedented increase in the availability of data sets that
are inherently global and temporally evolving, from remotely sensed networks to climate …

Gaussian process regression is widely used in many fields, for example, machine learning,
reinforcement learning and uncertainty quantification. One key component of Gaussian …

This paper proposes and studies a numerical method for approximation of posterior
expectations based on interpolation with a Stein reproducing kernel. Finite-sample-size …

Random smoothing data augmentation is a unique form of regularization that can prevent
overfitting by introducing noise to the input data, encouraging the model to learn more …

Gaussian processes (GPs) are used to make medical and scientific decisions, including in
cardiac care and monitoring of atmospheric carbon dioxide levels. Notably, the choice of GP …

This paper addresses the problem of finding parametric constraints that ensure the validity of
the multivariate Matérn covariance for modeling the spatial correlation structure of …

The SPDE Approach to Matern Fields: Graph Representations Page 1 Statistical Science
2022, Vol. 37, No. 4, 519–540 https://doi.org/10.1214/21-STS838 © Institute of Mathematical …

The Matérn family of covariance functions has played a central role in spatial statistics for
decades, being a flexible parametric class with one parameter determining the smoothness …