Bayesian Methods for Medical Statistics
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|
Semester 2 - 2017
On successful completion of this course, students will be able to:
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.
per Week for
|Timetable||2017 Course Timetables for BIOS6130|