MATH3871 is a 3rd year course. See the course overview below.
Units of credit: 6
Prerequisites: MATH2801 or MATH2901
Cycle of offering: Course not offered every year; Term 3 in Trimester.
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.
After describing the fundamentals of Bayesian Inference this course will examine specification of prior distributions, links between Bayesian and frequentist inference, Bayesian model comparison and Bayesian computational methods. Markov chain Monte Carlo (MCMC) methods for computations will be described and implemented using statistical packages including WinBUGS. We will illustrate the advantages of the Bayesian approach by describing Bayesian inferential methods for a variety of models, including linear models and various kinds of hierarchical structured models including mixture models.