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 …

The use of complex statistical models has recently increased substantially in the context of
species distribution behavior. This complexity has made the inferential and predictive …

Modeling spatial and spatio-temporal continuous processes is an important and challenging
problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential …

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

Priors are important for achieving proper posteriors with physically meaningful covariance
structures for Gaussian random fields (GRFs) since the likelihood typically only provides …

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 …

A stationary spatial model is an idealization and we expect that the true dependence
structures of physical phenomena are spatially varying, but how should we handle this non …

Gaussian random fields (GRFs) play an important part in spatial modelling, but can be
computationally infeasible for general covariance structures. An efficient approach is to …

In many environmental applications involving spatially‐referenced data, limitations on the
number and locations of observations motivate the need for practical and efficient models for …

Potentially toxic element (PTE) pollution is considered as the main soil environmental
problem in the world. Source apportionment and spatial pattern of soil PTEs are essential for …