Two short-courses on Bayesian Modelling and Computation and Hierarchical Modelling of Spatial and Temporal Data

June 1-4, 2015

University of Southampton, UK


Course 1: Bayesian Modelling and Computation

June 1-2, 2015

The first short-course on "Bayesian Modelling and Computation" is aimed at applied scientists who are thinking of using Bayesian methods and would like to receive a gentle introduction with a large practical component.

No previous knowledge of Bayesian methods is necessary. However, some familiarity with standard probability distributions (normal, binomial, Poisson, gamma) and standard statistical methods such as multiple regression will be assumed.

Theory lectures on the Bayes theorem, elements of Bayesian inference, choice of prior distributions and introduction to MCMC will be followed by hands-on experience using R and the WinBUGS software. Some of the data analysis examples discussed here will be enhanced by using spatial statistics methods in the second course.

More advanced methods using Hamiltonian MCMC, reversible jump, INLA, Variational Bayes, and ABC will also be introduced.

Please see a tentative programme.

Course 2: Hierarchical Modelling of Spatial and Temporal Data

June 3-4, 2015

This course will provide an overview of current ideas in statistical inference methods appropriate for analysing various types of spatially point referenced data, some of which may also vary temporally.

The course begins with an outline of the three types of spatial data: point-level (geostatistical), areal (lattice) and spatial point process, illustrated with examples from environmental pollution monitoring and epidemiological disease mapping.

Exploratory data analysis tools and traditional geostatistical modelling approaches (variogram fitting, kriging, and so forth) are described for point referenced data, along with similar presentations for areal data models. These start with choropleth maps and other displays and progress towards more formal statistical concepts, such as the conditional, intrinsic, and simultaneous autoregressive (CAR, IAR, and SAR) models so often used in conjunction with spatial disease mapping.

The heart of the course will cover hierarchical modelling for spatial response data, including Bayesian kriging and lattice modelling. More advanced issues will also be covered, such as nonstationarity (mean level depending on location) and anisotropy (spatial correlation depending on direction as well as distance). Bayesian methods will also be discussed for modelling data that are spatially misaligned (say, with one variable measured by post-code and another by census tract), since they are particularly well-suited to sorting out complex interrelationships and constraints.

The course concludes with a brief discussion of spatio-temporal and spatial survival models, both illustrated in the context of cancer control and epidemiology. Computer implementations for the models via the WinBUGS and R packages will be described throughout.

Please see a tentative programme.

Participants are encouraged to buy the book Hierarchical Modeling and Analysis for Spatial Data, co-authored by Professor Gelfand.

These courses are likely to be very popular, as when these were run biennially since 2005.