Announce: A two-day introductory Bayesian modelling and computation course followed by a one-day course on inference methods for big data
[Apologies for cross-posting]
Lecturers: Prof Dipak Dey (University of Connecticut, USA) and Sujit Sahu (University of Southampton, UK)
Date: September 14-16, 2016
Venue: University of Southampton, UK.
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Course 1: Bayesian Modelling and Computation, September 14-15, 2016.
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.
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Course 2: Inference methods for Big Data, September 16, 2016.
Big data is now a reality in many disciplines where decisions must be based on the science of analyzing and handling big data. The main aim of this 1-day short-course is to develop rigorous exploratory and inference methods for analyzing big data. Beginning with an introduction to big data, we aim to cover a range of topics such as representation, clustering, classification, and imputation of missing data. We then develop mechanisms for performing Bayesian analysis using prior distributions. A number of examples from Bioinformatics will be used to illustrate the main ideas and the inference mechanisms.
Please visit the web page http://www.southampton.ac.uk/~sks/2016course/ for further information including details for fees and registration.
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