[Apologies for Cross-Posting]
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Announce: Two Short-courses on Bayesian Modelling and Computation and Hierarchical Modelling of Spatial and Temporal Data
Lecturers: Alan Gelfand (Duke University, USA) and Sujit Sahu (University of Southampton, UK)
Date: June 1-4, 2015
Venue: University of Southampton, UK.
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Course 1: Introduction to Bayesian Modelling and Computation.
Date: June 1-2, 2015 (Monday-Tuesday)
The first short-course 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, 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.
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Course 2: Hierarchical Modelling of Spatial and Temporal Data.
Date: June 3-4, 2015 (Wednesday-Thursday)
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 will cover hierarchical modelling for spatial response data, including Bayesian kriging and lattice modelling with many practical examples. Hands-on training using R and WinBugs will be provided.
Participants should have a good understanding of mathematical statistics (such as a typical Bachelor's degree in mathematics, statistics or a related discipline from a UK university). In addition, basic familiarity with standard statistical models such as multiple linear regression and computing will be required. Attending the preceding two-day course on Bayesian statistics and MCMC will help prepare for this course, although that is not an absolute pre-requisite.
If you are unsure about the suitability of your background for the course, please email Prof Sujit Sahu ([log in to unmask]) who can advise.
For further information and registration please visit:
http://www.southampton.ac.uk/~sks/course2015/index.php
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