On the occasion of the Spatial Statistics' 10th Anniversary, I reflect on the past and present of
Bayesian disease mapping and look into its future. I focus on some key developments of …

We present a general coregionalization framework for developing coregionalized
multivariate Gaussian conditional autoregressive (cMCAR) models for Bayesian analysis of …

Entropy measures are standard tools in environmental and ecological sciences to describe
the heterogeneity of data. This paper reviews a selection of spatial entropy indices, some of …

Disease Mapping: From Foundations to Multidimensional Modeling guides the reader from
the basics of disease mapping to the most advanced topics in this field. A multidimensional …

To analyze and project age-specific mortality or morbidity rates ageperiod-cohort (APC)
models are very popular. Bayesian approaches facilitate estimation and improve predictions …

Some recent work on conditional formulation of multivariate Gaussian Markov random fields
is presented. The focus is on model constructions by compatible conditionals and …

This paper concerns with multivariate conditional autoregressive models defined by linear
combination of independent or correlated underlying spatial processes. Known as linear …

Gaussian Markov random fields (GMRF) and their multivariate extensions (MGMRFs) are
powerful tools for modelling probabilistic interactions of directly related variables. As an …

This article concerns with conditionally formulated multivariate Gaussian Markov random
fields (MGMRF) for modeling multivariate local dependencies with unknown dependence …

In this work, we present a new approach for constructing models for correlation matrices with
a user-defined graphical structure. The graphical structure makes correlation matrices …