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Lectures and Exercises

For the computer exercises the participants are requested to bring their own laptops (with R installed).

Planned lectures and computer exercises

In his lectures, Roger Bivand will cover topics including spatial data analysis, representing space, interfacing geographical information systems, space-time data, spatial statistics overview (point patterns, geostatistics, areal data). Only some topics will be covered in any depth, the others will just be mentioned briefly. The focus in the computer exercises will be on the ctv package in R, and you are suggested to install it before the conference using the following code in R:
install.packages("ctv")
library("ctv")
install.views("Spatial", coreOnly=TRUE)

Konstantin Krivoruchko will discuss the use of GIS and spatial statistics for radioecological modeling and mapping using Chernobyl- and Fukushima-related data, focusing on public health protection. ArcGIS Geostatistical Analyst has all the necessary tools and models for the detailed spatial data exploration and for optimal statistical interpolation. In addition to interpolation techniques, I will show examples of statistical spatial data analysis using freeware R statistical software packages and their integration with ArcGIS software. Also hands-on exercises using ArcGIS Geostatistical Analyst are planned.

In his lectures, Daniel Simpson will discuss approximate Bayesian inference for a class of models named `latent Gaussian models' (LGM). LGM's are perhaps the most commonly used class of models in statistical applications. It includes, among others, most of (generalized) linear models,(generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models,log-Gaussian Cox processes and geostatistical and geoadditive models.

The concept of LGM is intended for the modeling stage, but turns out to be extremely useful when doing inference as we can treat models listed above in a unified way and using the *same* algorithm and software tool. Our approach to (approximate) Bayesian inference, is to use integrated nested Laplace approximations (INLA). Using this new tool, we can directly compute very accurate approximations to the posterior marginals. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, our approximations provide more precise estimates in seconds or minutes. Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way,and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged.

In these lectures he will introduce the required background and theory for understanding INLA, and will end these lectures illustrating INLA on a range of examples in R (see www.r-inla.org), including spatial models.
You are suggested to install r-inla on your laptop before the conference (available at www.r-inla.org)