Practical Bayesian Data Analysis
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. 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. The course will include a practical introduction to WinBUGS and will also make use of the R package. Participants will have the opportunity to use WinBUGS in the practical sessions.
Who Should Attend?
Statisticians and data analysts who wish to use a Bayesian approach in analysing their data. No prior knowledge of WinBUGS or R will be assumed.
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.
What Do We Cover?
- Likelihood, prior and posterior distributions and the use of Bayes' theorem
- Bayesian analysis of single-parameter models and simple multi-parameter models
- Conjugate, vague and informative priors
- Simulation of posterior distributions; posterior summaries
- MCMC, Gibbs sampling; MCMC diagnostics
- Linear models, generalised linear models and model selection
- Model checking
- Hierarchical Bayesian models
- Using WinBUGS
This course has practical exercises written for: WinBUGS supplemented by R where required