In this article, we provide a comprehensive review of space–time covariance functions. As
for the spatial domain, we focus on either the d‐dimensional Euclidean space or on the unit …

Gaussian processes and random fields have a long history, covering multiple approaches to
representing spatial and spatio-temporal dependence structures, such as covariance …

The series aims to: present current and emerging innovations in GIScience; describe new
and robust GIScience methods for use in transdisciplinary problem solving and decision …

One can slightly modify the usual L, differentiability constraints of Sobolev types on densities
with the help of Besov norms. This has the advantage, using the wavelets characterization of …

Introducing flexible covariance functions is critical for interpolating spatial data since the
properties of interpolated surfaces depend on the covariance function used for Kriging. An …

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 …

Accurately capturing the architecture of single lithium-ion electrode particles is necessary for
understanding their performance limitations and degradation mechanisms through multi …

Gaussian processes are arguably the most important model class in spatial statistics. They
encode prior information about the modeled function and can be used for exact or …

It is well known that the microstructure of the active material in lithium-ion battery electrodes
has a strong influence on the battery's performance. In order to improve functional properties …

Statistical models used in geophysical, environmental, and climate science applications
must reflect the curvature of the spatial domain in global data. Over the past few decades …