Bayesian Statistical Methods

Postgraduate | 2026

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Area/Catalogue
BIOL 5031
Course ID icon
Course ID
203057
Level of study
Level of study
Postgraduate
Unit value icon
Unit value
6
Course level icon
Course level
5
Study abroad and student exchange icon
Inbound study abroad and exchange
Inbound study abroad and exchange
The fee you pay will depend on the number and type of courses you study.
No
University-wide elective icon
University-wide elective course
No
Single course enrollment
Single course enrolment
No
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Note:
Course data is interim and subject to change

Course overview

The aim of this course is to achieve an understanding of the logic of Bayesian statistical inference, i.e. the use of probability models to quantify uncertainty in statistical conclusions, and acquire skills to perform practical Bayesian analysis relating to health research problems. Topics will include simple one-parameter models with conjugate prior distributions; standard models containing two or more parameters, including specifics for the normal location-scale model; the role of non-informative prior distributions; the relationship between Bayesian methods and standard 'classical' approaches to statistics, especially those based on likelihood methods; computational techniques for use in Bayesian analysis, especially the use of simulation from posterior distributions, with emphasis on the WinBUGS package as a practical tool; and application of Bayesian methods for fitting hierarchical models to complex data structures.

Course learning outcomes

  • See Study Guides at https://url.au.m.mimecastprotect.com/s/cLi1CoV1Q6CrwYl9qTDtxUpG_gI?domain=bca.edu.au/

Prerequisite(s)

N/A

Corequisite(s)

N/A

Antirequisite(s)

N/A