Bayesian Methods

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

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.

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.