The Bayesian approach to statistics assigns probability distributions to both the data and unknown parameters in the problem.  This way, we can incorporate prior knowledge on the unknown parameters before observing any data.  Statistical inference is summarised by the posterior distribution of the parameters after data collection, and posterior predictions for new observations.  The Bayesian approach to statistics is very flexible because we can describe the probability distribution of any function of the unknown parameters in the model.  Modern advances in computing have allowed many complicated models, which are difficult to analyse using ‘classical’ (frequentist) methods, to be readily analysed using Bayesian methodology.  

The aim of this course is to equip students with the skills to perform and interpret Bayesian statistical analyses.  The first part of the course is devoted to describing the fundamentals of Bayesian inference by examining some simple Bayesian models.  More complicated models will then be explored, including linear regression and hierarchical models in a Bayesian framework.  Bayesian computational methods, especially Markov Chain Monte Carlo methods will progressively be introduced as motivated by the models discussed.   Emphasis will also be placed on model checking and evaluation.