Coming up with Bayesian models for spatial data is easy, but performing inference with them
can be challenging. Writing fast inference code for a complex spatial model with realistically …

Summary Continuously indexed Gaussian fields (GFs) are the most important ingredient in
spatial statistical modelling and geostatistics. The specification through the covariance …

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

The principles behind the interface to continuous domain spatial models in the RINLA
software package for R are described. The integrated nested Laplace approximation (INLA) …

Deep neural network models have become ubiquitous in recent years and have been
applied to nearly all areas of science, engineering, and industry. These models are …

Supplement to “Strictly and non-strictly positive definite functions on spheres”. Appendix A
states and proves further criteria of Pólya type, thereby complementing Section 4.2 …

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 …

Estimates of ground-level ozone concentrations are necessary to determine the human
health burden of ozone. To support the Global Burden of Disease Study, we produce yearly …

Stationary Stochastic Processes Page 1 Lindgren Georg Lindgren Stationary Stochastic
Processes Theory and Applications Stationary Stochastic Processes Texts in Statistical Science …

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 …