Available in 2018

Course handbook


The course introduces students to the basic principles of Bayesian statistics that have applications in the field of medicine.

Availability2018 Course Timetables

WebLearn GradSchool

  • Semester 2 - 2018

Learning outcomes

On successful completion of the course students will be able to:

1. Understand Bayes' rule, prior distributions and their applications to medical statistics;

2. Be able to apply Bayesian methods to medical data using SAS and WINBUGS software;

3. Interpret credible intervals and probabilities from a Bayesian standpoint

4. Impute data using MCMC Bayesian methodology


Topics covered in this course will include the principles of prior and posterior distributions, Bayes' rule for statistical inference, conjugate and non-conjugate priors, the Gibbs sampler, Wishart and inverse Wishart distributions, the Metropolis and Metropolis-Hastings algorithms, Jeffries invariant prior, hierarchical linear models from a Bayesian perspective, the concept of  credible intervals, exchangeable prior models for robust inference, Bayesian mixture models and Markov Chain Monte Carlo (MCMC) models.




Students must have successfully completed BIOS6170 to enrol in this course.

Assessment items

Essay: Essay

Contact hours

WebLearn GradSchool

Online Activity

Online 6 hour(s) per Week for Full Term

As an indication only, students may expect to spend 8-10 hours per week on study.