Handbook

After describing the fundamentals of Bayesian inference, this course will examine several detailed aspects including: the specification of prior and posterior distributions, Bayesian decision theoretic concepts, the ideas behind Bayesian hypothesis tests, model choice and model averaging, and various model types such as hierarchical and mixture models. An important part of Bayesian inference is the requirement to numerically evaluate complex integrals on a routine basis. Accordingly this course will also introduce the ideas behind Monte Carlo integration, importance sampling, rejection sampling, Markov chain Monte Carlo samplers such as the Gibbs sampler and the Metropolis-Hastings algorithm. This course is only available to students enrolled in the Online Masters or Graduate Diploma of Data Science.

Material is delivered 100% online through a series of self-guided lessons which include challenges, practice questions and videos. Questions can be asked on the discussion forum. Webinars will be held to guide students through the material and to help answer further questions that cannot be raised in the discussion forum.