MATH5960 is a Honours and Postgraduate Coursework Mathematics course.

Units of credit: 6

Exclusions: MATH3871 (jointly taught with MATH5960), ZZSC5960

Cycle of offering: Term 3 

Graduate attributes: The course will enhance your research, inquiry and analytical thinking abilities.

More information: The course outline (PDF) contains information about course objectives, assessment, course materials and the syllabus.Outlines will be made available closer to the commencement of term offering.

Important additional information as of 2023

UNSW Plagiarism Policy

The University requires all students to be aware of its policy on plagiarism.

For courses convened by the School of Mathematics and Statistics no assistance using generative AI software is allowed unless specifically referred to in the individual assessment tasks.

If its use is detected in the no assistance case, it will be regarded as serious academic misconduct and subject to the standard penalties, which may include 00FL, suspension and exclusion.

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 MATH5960, you can log into UNSW Moodle for this course.

Course overview

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.