Bayesian Methods

• Credit value: 15 credits at Level 7
• Tutor: Swati Chandna

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

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.