Practicalities and application information
SoftWare Installation Instructions for course participants
Map to Crop & Food Research, Lincoln
Map to Agresearch Palmerston North
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In December in New Zealand, we will be running an Australasian version of the popular Practical Bayesian Analysis course that has been run by the Statistical Services Centre (University of Reading, UK) for the past few years (http://www.rdg.ac.uk/ssc/courses/bayes.html).
This course is aimed at statisticians, people involved in data-analysis, and those who use Bayesian methods as implemented in packages such as those for QTL analysis. The course trainers are experienced teachers, and the emphasis in the course is on a practical explanation of Bayesian ideas, rather than more theoretical aspects of Bayesian methods.
Participants will learn how to use the WinBUGS software for Bayesian analysis. The course will also use the R package. No previous knowledge of either package is assumed.
The course will be run over 3 days, starting mid-morning on the first day and finishing mid-day on the third day, to allow for travel.
The course fee is NZ$850, and covers course notes, a CD with example data-sets, lunches and morning/ afternoon teas. The course will be run at Crop & Food Research in Lincoln (outside Christchurch) on Wed 12th Dec - Fri 14th Dec, and then again at AgResearch in Palmerston North on Mon 17th Dec- Wed 19th Dec
Further information, from the Reading SSC Website:
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Course outline
Bayesian statistical methods utilise prior information about model parameters in the inference process. Although the idea is not new, it is only relatively recently that modern computational methods have made Bayesian data analysis a practical possibility. In particular, simulation methods such as Markov chain Monte Carlo (MCMC), implemented in the WinBUGS software, enable the Bayesian analysis of an exceptionally wide range of statistical models. Even when no prior information is available, the flexibility offered by this approach far exceeds that of any other modelling framework.
The emphasis in this course is on practical data analysis, although the essential theory will be explained. Starting from simple single parameter models, the course will develop an approach to the analysis of quite complex data structures. The course will include a practical introduction to WinBUGS and will also make use of the R package. |
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Who should attend?
Statisticians and data analysts who wish to use a Bayesian approach in analysing their data. Even those who are not comfortable with using prior information in their analysis will find the flexible modelling made possible by MCMC methods a powerful tool. No prior knowledge of WinBUGS or R will be assumed. |
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How you will benefit?
You will extend your data analysis skills to cover a very wide class of modelling, including the use of prior information. You will learn how to use specialised software for Bayesian data analysis. |
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Course content
- Likelihood, prior and posterior distributions and the use of Bayes' theorem
- Bayesian analysis of single parameter models and simple multi-parameter models
- Conjugate, non-informative and informative priors
- Simulation of posterior distributions; posterior summaries
- Hierarchical Bayesian models
- Sampling from posteriors: Gibbs and Metropolis-Hastings sampling
- MCMC diagnostics and convergence issues
- Case studies with WinBUGS: more complex models, including mixed effects models and non-linear models
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Course Lecturers
Bob Burn (http://www.rdg.ac.uk/ssc/staff/rwb.html).
Fiona Underwood
( http://www.rdg.ac.uk/ssc/staff/fmu.html ) |
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