STAT41070

This module will equip students with the knowledge required to practically use standard statistical data analysis tools within a Bayesian framework. The module will focus on data analysis examples, using modern Bayesian statistical computing tools, e.g. R, Nimble, or Stan.

Students will learn how to implement standard statistical models such as linear regression, Poisson regression, logistic regression and Gaussian processes in a Bayesian framework. Application of these methods to high-dimensional, complex data will be considered, e.g. shrinkage priors in the context of regression or handling of missing data.

Students will use modern computational tools such as the Gibbs sampler and the Metropolis- Hastings algorithm. Through implementing these models, aspects such as convergence diagnostics, model checking and posterior predictive checks will be considered. Throughout, real data analysis examples will be used to motivate and illustrate the methods and theory of practical Bayesian data analysis.