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

The integrated nested Laplace approximation (INLA) provides an interesting way of
approximating the posterior marginals of a wide range of Bayesian hierarchical models. This …

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

Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed,
practically oriented & innovative presentation of the combination of Bayesian methodology …

Spatial data is crucial to improve decision-making in a wide range of fields including
environment, health, ecology, urban planning, economy, and society. Spatial Statistics for …

Geostatistical models have traditionally been stationary. However, physical knowledge
about underlying spatial processes often requires models with non-stationary dependence …

The challenges of estimating hierarchical spatial models to large datasets are addressed.
With the increasing availability of geocoded scientific data, hierarchical models involving …

Spatial analysis has grown at a remarkable rate over the past two decades. Fueled by
sophisticated GIS software and inexpensive and fast computation, collection of data with …

During the last three decades, Bayesian methods have developed greatly in the field of
epidemiology. Their main challenge focusses around computation, but the advent of Markov …