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The aim of this unit is to provide a solid understanding of how Bayesian methods are applied to solve data-related problems in medicine and health sciences. You will explore the differences between Bayesian and Frequentist (also known as classical or approximation-based) statistical approaches, and learn how to apply Bayesian methods. This unit covers: Bayesian model development using prior knowledge (single and multiple parameter models, such as generalised linear regression, mixed-models); the use of Directed acyclic graph (DAG) in the context of Bayesian hierarchical modelling; designing clinical trials and calculating sample sizes using Bayesian methods; and Bayesian model choice/selection, including computational techniques. The course will use R programming language and Stan, though no prior knowledge of Stan is required.
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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