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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. This unit covers: 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 non-informative 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; application of Bayesian methods for fitting hierarchical models to complex data structures. R will be used for simulations and model fitting using MCMC routines.
Study level | Postgraduate |
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Academic unit | Public Health |
Credit points | 6 |
Prerequisites:
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BSTA5210 or BSTA5211 or (BSTA5007 and BSTA5008) |
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Corequisites:
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None |
Prohibitions:
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None |
Assumed knowledge:
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None |
At the completion of this unit, you should be able to:
This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.
The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
Session | MoA ? | Location | Outline ? |
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Semester 2 2025
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Online | Camperdown/Darlington, Sydney |
Outline unavailable
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Session | MoA ? | Location | Outline ? |
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Semester 2 Early 2020
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Online | Camperdown/Darlington, Sydney |
Outline unavailable
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Semester 2 2022
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Online | Camperdown/Darlington, Sydney |
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