Curtin University – Australia
Spatial Point Patterns: Methodology and Applications with R expand_more
Familiarity with basic statistical concepts, and with the R language for statistical computation, is assumed.Jointly with Ege Rubak (Aalborg University – Denmark).
St. Michael's Hospital and University of Toronto – Canada
Statistical models and inference for spatio-temporal areal data expand_more
KAUST – Saudi Arabia
Spatial and spatio-temporal models using the SPDE-approach expand_more
In these lectures we will discuss how to construct (Gaussian) spatial models, in space and space-time, using stochastic partial differential equations (the so-called SPDE approach), and how to do Bayesian analysis of these models efficiently using R-INLA. The SPDE-approach comes with several benefites. First, it generates models, like the Gaussian fields with the Matern covariance function, that is extremely well suited for practical computations through sparse precision matrices. Secondly, it is the natural framework for constructing non-stationary models, like accounting for barriers, and non-separable models in space-time. addJointly with Haakon Bakka (KAUST – Saudi Arabia).
AgroParisTech – France
New trends in spatio-temporal geostatistics expand_more