This course aims at offering students rigorous knowledge of Bayesian statistical theory and methods, developing students’ abilities of interpreting and communicating results, as well as training students to apply software packages such as R or Matlab to fit Bayesian models and conduct Bayesian analyses.

Note: To pass the course, apart from obtaining a minimum of 40% in the overall mark, a student must also obtain a minimum mark of 30% in both continuous assessment and examination components.