Bayesian Inference and Computation - MATH3871

Faculty: Faculty of Science

School: School of Mathematics and Statistics

Course Outline:

Campus: Sydney

Career: Undergraduate

Units of Credit: 6

EFTSL: 0.12500 (more info)

Indicative Contact Hours per Week: 4

Enrolment Requirements:

Prerequisite: MATH2801 or MATH2901

Equivalent: MATH5960

CSS Contribution Charge: 2 (more info)

Tuition Fee: See Tuition Fee Schedule

Further Information: See Class Timetable

View course information for previous years.


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.
Faculty of Science

Study Levels

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