This course will provide you with an introduction to the Bayesian framework in statistics, including the differences between Bayesian approaches and the more standard frequentist approach. It will cover basic single and multi-parameter models, regression, Bayesian estimation (including MCMC simulation techniques), and hierarchical models. You will be introduced into Bayesian modelling through the use of the R programming language and JAGS as computational tools. The emphasis in the course is in encouraging you to apply Bayesian methods in various environments including business and health sciences.

Please note that if you take this course for a bachelor honours program, your overall mark in this course will be one of the course marks that will be used to calculate the weighted average mark (WAM) that will determine your award level. (This applies to students who commence enrolment in a bachelor honours program from 1 January 2016 onwards. See the WAM information web page for more information.)