Dear All,
We have spaces available on our 2 part course Part 1: Bayesian Modelling and computation; Part 2: Inference from big data, details are below, if you have any further queries or with to sign up either email: [log in to unmask] or visit our online store:
http://go.soton.ac.uk/7aj
http://go.soton.ac.uk/7ak
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 ([log in to unmask]) who can advise.
Kind Regards
Andrew
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