Kernel methods are among the most popular techniques in machine learning. From a
regularization perspective they play a central role in regularization theory as they provide a …

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 …

Spatially resolved genomic technologies have allowed us to study the physical organization
of cells and tissues, and promise an understanding of local interactions between cells …

Multi-output regression problems have extensively arisen in modern engineering
community. This article investigates the state-of-the-art multi-output Gaussian processes …

Experiments have long been used to study the relationship between a set of inputs to a
physical system and the resulting output. Termed physical experiments in this text, there is a …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

Conventional geostatistical methodology solves the problem of predicting the realized value
of a linear functional of a Gaussian spatial stochastic process S (x) based on observations …

With scientific data available at geocoded locations, investigators are increasingly turning to
spatial process models for carrying out statistical inference. Over the last decade …

Continuously indexed datasets with multiple variables have become ubiquitous in the
geophysical, ecological, environmental and climate sciences, and pose substantial analysis …

We introduce a flexible parametric family of matrix-valued covariance functions for
multivariate spatial random fields, where each constituent component is a Matérn process …