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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 MarkovChain 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 MetropolisHastings algorithm using OpenBugs (WinBUGs), Matlab or R;
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