The Mat\'ern model has been a cornerstone of spatial statistics for more than half a century.
More recently, the Mat\'ern model has been central to disciplines as diverse as numerical …

Stein importance sampling is a widely applicable technique based on kernelized Stein
discrepancy, which corrects the output of approximate sampling algorithms by reweighting …

In multivariate spatio-temporal analysis, we are faced with the formidable challenge of
specifying a valid spatio-temporal cross-covariance function, either directly or through the …

Gaussian processes are widely employed as versatile modelling and predictive tools in
spatial statistics, functional data analysis, computer modelling and diverse applications of …

Decades of research in spatial statistics have prompted the development of a wide variety of
models and methods whose primary goal is optimal linear interpolation (kriging), as well as …

Random fields on the sphere play a fundamental role in the natural sciences. This paper
presents a simulation algorithm parenthetical to the spectral turning bands method used in …

This article gives a narrative overview of what constitutes climatological data and their
typical features, with a focus on aspects relevant to statistical modeling. We restrict the …

The paper provides a way to model axially symmetric random fields defined over the two-
dimensional unit sphere embedded in the three-dimensional Euclidean space. Specifically …

This paper presents a theoretical analysis of numerical integration based on interpolation
with a Stein kernel. In particular, the case of integrals with respect to a posterior distribution …

This work provides theoretical foundations for kernel methods in the hyperspherical context.
Specifically, we characterise the native spaces (reproducing kernel Hilbert spaces) and the …