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The University of Southampton
Economic, Social and Political Sciences

Introduction to Bayesian Analysis Event

Mathematical Sciences
9 - 12 April 2013
University of Southampton Southampton SO17 1BJ UK

For more information regarding this event, please telephone Nicole Thompson – Vassel on +44 (0) 23 8059 9036 or email .

Event details

Two Short-courses on Introduction to Bayesian Analysis and MCMC, and Hierarchical Modelling of Spatial and Temporal Data, with lecturers Sujit Sahu (University of Southampton, UK) and Alan Gelfand (Duke University, USA)

Course 1: Introduction to Bayesian Analysis MCMC

Tuesday 9th April 2013

The first one-day short-course on "Introduction to Bayesian Analysis and MCMC" is aimed at statisticians who are thinking of taking the second course on spatial statistics but would like to go through a preparatory course providing 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.

This course can be taken without taking the three-day hierarchical modelling course, although preference will be given to participants opting for both courses.

Course 2: Hierarchical Modelling of Spatial and Temporal Data

Wednesday 10th April - Friday 12th April 2013

The 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 both univariate and multivariate 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, R, and S+SpatialStats packages will be described throughout. Participants will gain experience of using WinBUGS and R.

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

Registration and Costs

The registration fees are as follows:

Course 1:

- research students £150

- academics £200

- all others £250

Course 2:

- research students £450

- academics £600

- all others £750

University of Southampton staff will receive a 30% discount on the above prices.

The fee will include course materials, computing facilities, lunch and refreshments each day. The number of spaces is limited, so an early registration is advised.

Payments can be made by the University online store:

Payments for course 1

Participants are required to book their own accommodation, please refer to the link for information on accommodation

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