STATS 3023 - Computational Bayesian Statistics III

The aim of this course is to equip students with the theoretical knowledge and practical skills to perform Bayesian inference in a wide range of practical applications. Following an introduction to the Bayesian framework, the course will focus on the main Markov chain Monte Carlo algorithms for performing inference and will consider a number of models widely used in practice. Topics covered are: Introduction to Bayesian statistics; model checking, comparison and choice; introduction to Bayesian computation; Gibbs sampler; Metropolis-Hastings algorithm; missing data techniques; hierarchical models; regression models; Gaussian process models.