With the expansion in the quantity and types of biodiversity data being collected, there is a
need to find ways to combine these different sources to provide cohesive summaries of …

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 …

While global patterns of human genetic diversity are increasingly well characterized, the
diversity of human languages remains less systematically described. Here, we outline the …

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 …

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

We describe NIMBLE, a system for programming statistical algorithms for general model
structures within R. NIMBLE is designed to meet three challenges: flexible model …

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

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

Mountain ranges harbour exceptionally high biodiversity, which is now under threat from
rapid environmental change. However, despite decades of effort, the limited availability of …

Supplement to “Penalising Model Component Complexity: A Principled, Practical Approach
to Constructing Priors”. The supplementary material contains the proofs of all theorems …