Professor Dipak Dey and Prof Sujit Sahu
September 14-16, 2016
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.
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.
Who should attend? The two courses are primarily aimed at statisticians who wish to use Bayesian methods in their big data analysis and modelling problems. The courses will be suitable for statisticians from government departments, practitioners from industry, research students at all levels, and academic researchers from other disciplines but with strong backgrounds in statistics.
Pre-requisite for Course 1 (Bayesian): No previous knowledge of Bayesian methods is necessary. Participants should have a reasonable 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. Basic familiarity with the R-software package will be an advantage.
Pre-requisite for Course 2 (Big Data): Participation in the previous short-course on Bayesian methods. This can only be waived if a participant has taken a similar course in Southampton or elsewhere or have the necessary background. Please email Professor Sahu (S.K.Sahu@soton.ac.uk) who can advise.
Tentative Programme
Programme
| Course 1: Bayesian Modelling and Computation: Programme on September 14, Wednesday. |
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| 9:0AM-9:30AM | Registration |
| Morning Session 9:30AM -12:30PM,
Coffee Break 11-11:30AM |
Review of Bayesian principles; prior specifications; Bayesian model comparison, Bayes factor, DIC.
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| Lunch Break 12:30PM-1:30PM | |
Afternoon Session
1:30PM -4:30PM,
Tea Break: 3-3:30PM |
Bayesian computation, Importance sampling, Rejection Sampling, Monte Carlo integration; Metropolis-Hastings Algorithm, Gibbs sampling and MCMC (theory and implementation); examples; computer exercises with R.
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| Course 1: Programme on September 15, Thursday. |
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| Morning Session 9:30AM-12:30PM Coffee Break 10:30-11AM |
Introduction to WinBUGS and Open BUGS.
Hierarchical modeling, random effects, missing data, latent variables, Dynamic models, Changepoint Models, Mixture models, Errors in variables models.
Practical session preferably using your own computer.
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| Lunch Break 12:30PM-1:30PM | |
Afternoon Session 1:30PM -4:30PM
Tea Break: 2:30-3PM | Advanced Bayesian Computation: Pseudo Bayes factor for model selection, Reversible Jump MCMC; Variational Bayes, Approximate Bayesian Computation, Laplace approximation and INLA.
Practical session preferably using your own computer. Discussion with the course lecturers regarding your own research problems.
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| Course 2: Inference methods for big data, September 16, Friday |
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| 9:0AM-9:30AM | Registration |
| Morning Session 9:30AM -12:30PM Coffee Break 10:30-11AM |
Introduction to multivariate analysis and high dimensional data. Introduction to Regularized Regression; Ridge Regression and Lasso.
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| Lunch Break 12:30PM-1:30PM | |
| Afternoon Session 1:30PM -4:30PM |
Principal Component and Canonical Correlation Analysis. Matrix Completion and Singular Value Decomposition.
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Registration Information
Course 1: Bayesian Modelling and Computation, September 14-15, 2016.
| Research students | £300 |
| Academics | £400 |
| All others | £500 |
Course 2: Inference methods for big data, September 16, 2016
| Research students | £200 |
| Academics | £250 |
| All others | £300 |
- The fee will include course materials, computing facilities, lunch and refreshments each day.
- University of Southampton staff and students will receive a 30% discount on the above prices.
Please email the professional training secretary if you require any assistance.
Payments can be made by the University online store:
- The number of spaces is limited, so an early registration is advised.
- Fees can be refunded in full if cancelled before August 14, 2016.
- Participants are required to book their own accommodation.
About the Lecturers
Professor Dipak Dey is a Board of Trustees Distinguished Professor in Statistics Department and Associate Dean in the College of Liberal Arts and Science at University of Connecticut. Prof Dey is an international expert on Bayesian modelling with focus on interdisciplinary research. He has co-authored more than 250 research articles, 9 books and edited volumes involving applications in categorical and longitudinal data, classification and clustering, spatial-temporal survival data analysis and the modelling extreme events. His research interests include: Biometry, Bioinformatics, Biostatistics, Chemometrics, Data Mining, Environmetrics, Morphometry, and Population Genetics. He has also presented similar short-courses very successfully to participants in many universities around the world.
Prof Sujit Sahu (University of Southampton) is an expert in model based Bayesian data analysis and has 25 years’ experience in this area of research. His research strength is in practical hierarchical Bayesian modelling and MCMC computation. He has successfully delivered similar short-courses in Bayesian statistics biennially since 2005 together with Prof Alan Gelfand in Southampton and also on his own in Australia, Chile, Italy and Spain.
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