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Unit of study_
BSTA5014: Bayesian Statistical Methods
2022 unit information
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.
Unit details and rules
Managing faculty or University school:
Public Health
| Code | BSTA5014 |
|---|---|
| Academic unit | Public Health |
| Credit points | 6 |
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Prerequisites:
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(PUBH5010 or BSTA5011 or CEPI5100) and BSTA5002 and (BSTA5210 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:
- LO1. Explain the difference between Bayesian and frequentist concepts of statistical inference.
- LO2. Demonstrate how to specify and fit simple Bayesian models with appropriate attention to the role of the prior distribution and the data model.
- LO3. Explain how these generative models can be used for inference, prediction, and model criticism.
- LO4. Demonstrate proficiency in using statistical software packages (R and Stan) to specify, fit, diagnose, and compare models.
- LO5. Engage in specifying, checking, and interpreting Bayesian statistical analyses in practical problems using effective communication with health and medical investigators.
Unit availability
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There are no availabilities for this year.
Modes of attendance (MoA)
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