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Overview

After describing the fundamentals of Bayesian inference, this course will examine the specification of prior and posterior distributions, Bayesian decision theoretic concepts, the ideas behind Bayesian hypothesis tests, model choice and model averaging, and evaluate the capabilities of several common 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, and use of the WinBuGS posterior simulation software. Pre-requisites: 24 units of level III mathematics or a degree in a numerate discipline or permission of the Head of Department.

Study Level

Postgraduate

Offering Terms

Term 3

Campus

Kensington

Delivery Mode

Fully on-site

Indicative contact hours

4

Course Outline

To access course outline, please visit:

Fees

Pre-2019 Handbook Editions

Access past handbook editions (2018 and prior)

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