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Bayesian Statistical Methods - BCA814
This unit provides a thorough introduction to the concepts and methods of modern Bayesian statistical methods. The overall aim is to develop students' ability to perform and critically interpret Bayesian statistical analyses in the medical research field. The concept of full probability modelling is introduced and developed through models with conjugate prior distributions. We explain the connection between Bayesian methods using noninformative priors and frequentist approaches. Bayesian methods for fitting hierarchical models to complex data structures are developed. Computational techniques for use in Bayesian statistics, especially Markov chain Monte Carlo, are covered with emphasis on the use of WinBUGS software.
| Credit Points: | 4 |
| When Offered: | S2 External - Session 2, External (On-campus dates: None) |
| Staff Contact(s): | Associate Professor Gillian Heller |
| Prerequisites: |
[BCA808 and BCA809] or [((STAT271 and STAT272) or STAT371 or STAT806 or STAT810) and (STAT411 or STAT811)] |
| Corequisites: | |
| NCCW(s): | |
| Unit Designation(s): | |
| Assessed As: | Graded |
| Offered By: | Department of Statistics Faculty of Science |
Timetable Information
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