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Bayesian Methods


Module description

We will cover the following topics:

  • The Bayesian Approach to Statistical Inference
  • Choice of Prior Distribution
  • Markov Chain Monte Carlo Methods
  • Bayesian Model Selection
  • Practical Bayesian Analysis in R

Learning objectives

By the end of this module, you will be able to:

  • appreciate the fundamental principles of Bayesian statistics
  • discuss the differences between Bayesian and traditional statistical methods
  • derive prior, posterior and predictive distributions for standard Bayesian models
  • derive summaries from the (fitted/estimated) posterior distribution
  • implement computational and simulation-based methods to Bayesian inference
  • undertake Bayesian decision theory and model choice
  • use a statistical package with real data to facilitate an appropriate analysis
  • write a report interpreting the results.