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Bayesian Statistical Methods - BCA814

The aim of this unit is to achieve an understanding of the logic of Bayesian statistical inference, i.e. the use of probability models to quantify uncertainty in statistical conclusions, and acquire skills to perform practical Bayesian analysis relating to health research problems. Topics include simple one-parameter models with conjugate prior distributions; standard models containing two or more parameters, including specifics for the normal location-scale model; the role of noninformative prior distributions; the relationship between Bayesian methods and standard 'classical' approaches to statistics, especially those based on likelihood methods; computational techniques for use in Bayesian analysis, especially the use of simulation from posterior distributions, with emphasis on the WinBUGS package as a practical tool; application of Bayesian methods for fitting hierarchical models to complex data structures.

Because of the multi-institutional nature of the BCA units, there is an early cut-off for enrolment in this unit. These dates are:
Session 1: 19 February 2018
Session 2: 23 July 2018

Credit Points: 4
When Offered:

S2 Online - Session 2, Online

Staff Contact(s): Professor Gillian Heller

[BCA808 and BCA809] or [((STAT271 and STAT272) or STAT371 or STAT306 or STAT806 or STAT810(Cr)) and (STAT411 or STAT811)] Prerequisite Information


Unit Designation(s):


Assessed As: Graded
Offered By:

Department of Mathematics and Statistics

Faculty of Science and Engineering

Course structures, including unit offerings, are subject to change.
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