Recent Bayesian methods for the analysis of infectious disease outbreak data using
stochastic epidemic models are reviewed. These methods rely on Markov chain Monte Carlo …

This article discusses and extends statistical models to jointly analyse the spatial variation of
rates of several diseases with common risk factors. We start with a review of methods for …

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 …

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

In recent years, disease map** studies have become a routine application within
geographical epidemiology and are typically analysed within a Bayesian hierarchical model …

Structured additive regression models are perhaps the most commonly used class of models
in statistical applications. It includes, among others,(generalized) linear …

Among the many uses of hierarchical modeling, their application to the statistical analysis of
spatial and spatio-temporal data from areas such as epidemiology And environmental …

Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics-a
very active area of research in which few up-to-date reference works are available. This is …

Effective population size is fundamental in population genetics and characterizes genetic
diversity. To infer past population dynamics from molecular sequence data, coalescent …