MATH3871 is a 3rd year course. See the course overview below.
Units of credit: 6
Prerequisites: MATH2801 or MATH2901
Equivalent courses: MATH5960
Cycle of offering: Term 3
Graduate attributes: The course will enhance your research, inquiry and analytical thinking abilities.
More information: This recent course handout (pdf) contains information about course objectives, assessment, course materials and the syllabus.
The Online Handbook entry contains information about the course. (The timetable is only up-to-date if the course is being offered this year.)
If you are currently enrolled in MATH3871, you can log into UNSW Moodle for this course.
This course aims to:
- Provide a strong background in the concepts and philosophy of Bayesian inference;
- Instill an appreciation of the benefits of the Bayesian framework;
- Provide extensive practical opportunities to implement Bayesian data analyses;
- Present an overview of research activity in this field.
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.