An announcement for two short-courses on Introduction to Bayesian Analysis and MCMC, and Hierarchical Modelling of Spatial and Temporal Data by Alan Gelfand (Duke University, USA) and Sujit Sahu (University of Southampton, UK), are to take place in Southampton on June 7-10, this year.
Course 1: Introduction to Bayesian Analysis and MCMC. Date: June 7, 2011 (Tuesday)
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. Date: June 8-10, 2011 (Wednesday-Friday)
This 3-day 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 both univariate and multivariate spatial response data, including Bayesian kriging and lattice modelling with applications. Hands-on training using R and Winbugs will be provided.
Participants should have a reasonable 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 one-day refresher course on Bayesian statistics and MCMC will help prepare for this course, although that is not a pre-requisite. Participants with a good grasp of the basic ideas in statistical inference can take this course without having to take the other one.
If you are unsure about the suitability of your background for the course, please email Dr Sujit Sahu (S.K.Sahu[chez]soton.ac.uk) who can advise. This course is likely to be very popular, as when it was previously run in 2009, hence early application is advised. The number of places in both the courses is limited to a maximum of 32.