Bayesian Methods for Medical Statistics

10 Units

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

Faculty Faculty of Health and Medicine
School School of Medicine and Public Health
Availability Semester 2 - 2018 (Online)
Learning Outcomes

On successful completion of this 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


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.

Course Materials
  • Statistical Decision and Bayesian Analysis
Timetable 2018 Course Timetables for BIOS6130
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