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 …

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

The integrated nested Laplace approximation (INLA) is a recent computational method that
can fit Bayesian models in a fraction of the time required by typical Markov chain Monte …

Spatial Data Science introduces fundamental aspects of spatial data that every data scientist
should know before they start working with spatial data. These aspects include how …

Recent wildfires in Australia have led to considerable economic loss and property
destruction, and there is increasing concern that climate change may exacerbate their …

Background Air pollution exposure has been associated with an increase in mortality rates,
but few studies have focused on life expectancy, and most studies had restricted spatial …

An intermediate-level treatment of Bayesian hierarchical models and their applications, this
book demonstrates the advantages of a Bayesian approach to data sets involving inferences …

INTRODUCTION One of the most general patterns in ecology is that diversity increases
toward the equator. In the ocean, however, mammal and bird richness generally peak in …

The idea behind any validation scheme in landslide susceptibility studies is to test whether a
model calibrated on a certain data can predict an unknown dataset of the same nature …