Bayesian Analysis using WinBUGS / OpenBUGS
Date: 4-5 April 2017
Duration: (2 days) 9am to 5pm
Instructor: Dr Guangquan Li and Dr Pete Philipson
Fee: £390 (£280 for those from educational, government and charitable institutions). The Cathie Marsh Institute (CMIST) offer five free places to research staff and students within the Faculty of Humanities at The University of Manchester.
Postgraduate students requesting a free place will be required to provide a letter of support from their supervisor.
Use of Bayesian methods is becoming increasingly widespread within quantitative social and health sciences, particularly for analysing data with complex structure, such as hierarchical or multilevel data. However, very few applied researchers have any formal training in Bayesian methods. This two-day course aims to introduce quantitative researchers to the basic principles of Bayesian inference and simulation-based methods for estimating Bayesian models, and to highlight some of the potential benefits that a Bayesian approach can offer. There is a large practical component to this course with time for hands-on data analysis using examples drawn mainly from the social and health sciences.
Statisticians, data analysts and quantitative researchers who are interested in finding out what Bayesian methods are all about, and how to implement some simple Bayesian models using the WinBUGS software. The course would also be of interest to researchers with experience of multi-level modelling using likelihood-based methods who wish to find out more about fitting and interpreting Bayesian versions of multi-level models. No previous experience of Bayesian methods or the WinBUGS software is required.
No previous experience of Bayesian methods or WinBUGS is necessary, although familiarity with standard statistical terminology and a good grasp of the basic principles of standard (maximum likelihood-based) linear and generalised linear regression models will be assumed. Participants will also be expected to be familiar with some common probability distributions (normal, binomial, Poisson). Some familiarity with the basic principles of multilevel modelling would also be useful, although not essential.
Chapters 11-6, 8 and 10 in the BUGS book.