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Bayesian Methods

Overview

  • Credit value: 15 credits at Level 7
  • Convenor: Dr Swati Chandna
  • Assessment: to be confirmed

Module description

In this module we cover:

  • 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.