The key operation in Bayesian inference is to compute high-dimensional integrals. An old
approximate technique is the Laplace method or approximation, which dates back to Pierre …

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 …

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 …

Abstract Direct Monte Carlo simulation for the reliability analysis of slope stability with
spatially variable soil properties suffers from a serious lack of efficiency when the probability …

Maps of infectious disease—charting spatial variations in the force of infection, degree of
endemicity and the burden on human health—provide an essential evidence base to …

The High Burden High Impact (HBHI) strategy for malaria encourages countries to use
multiple sources of available data to define the sub-national vulnerabilities to malaria risk …

We introduce a novel way to combine boosting with Gaussian process and mixed effects
models. This allows for relaxing, first, the zero or linearity assumption for the prior mean …

A popular approach for modeling and inference in spatial statistics is to represent Gaussian
random fields as solutions to stochastic partial differential equations (SPDEs) of the form L β …

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 …