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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 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;
Prerequisites: 

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Grading Policy: 

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Time: 

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Homeworks: 

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