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Bayesian Modelling and Computation Part One

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Bayesian Modelling and Computation Part One

The two courses are primarily aimed at statisticians who wish to use Bayesian methods in their data analysis and modelling problems. The courses will be suitable for statisticians from government departments, practitioners from industry, and research students at all levels. Academic researchers and scientists from other disciplines can also attend but should have a very strong background in statistics/mathematics to fully understand the whole course.

Please see more info page for citeria for booking on 2 part course.

Course 1: Bayesian Modelling and Computation, June12-14 2017 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 reversible jump and INLA will also be introduced.

Course 2: Hierarchical modelling of spatial and temporal data.

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.

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

Course Code


Course Dates

12th June 2017 – 14th June 2017

Places Available

Course Leader

Professor Sujit Sahu
Course Description

Pre-requisite for Course 1 (Hierarchical Bayesian Modelling):

Participants should have a very good understanding of mathematical statistics (such as a typical bachelor degree in mathematics, statistics or a related discipline from a UK university). Researchers from other disciplines must have a very good familiarity with standard probability distributions (normal, binomial, Poisson, gamma) and standard statistical methods such as multiple regression. No previous knowledge of Bayesian methods is necessary. Basic familiarity with the R-software package is also required. Participants should bring their own laptop.

Pre-requisite for Course 2 (Spatial Data Modelling):

A high level of training in Maths/Stats (such as a typical bachelor degree in mathematics, statistics or a related discipline from a UK university) is required to fully appreciate the materials to be presented in this course. Participation in the previous short-course on Bayesian methods is compulsory. This requirement can only be waived if a participant has taken a similar course in Southampton or elsewhere or has the necessary background. Please email Professor Sahu (S.K.Sahu@soton.ac.uk) who can advise.

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