Advanced Topics in Bayesian Statistics

Outline of the course

This course presents advanced topics in modern Bayesian statistics, including both the underlying theory and related practical issues.

• An introduction to Bayesian Statistics.
• Introduction of advanced stochastic simulation methods such as Markov-Chain Monte Carlo in a Bayesian context.
• Examples of inference for complicated models using their hierarchical representations. Noting to the importance of conditional independence in Bayesian statistical modelling.
• Illustration of the practical issues of application of such models and methods, with real data examples.
• Bayesian approaches to model selection.
•  Implementation of Gibbs sampling and the Metropolis-Hastings algorithm using OpenBugs (WinBUGs), Matlab or R;