Study method
100% online
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Assessments
Subjects may require attendance
Prior study
Required
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Subject details
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- Bayes theorem and the concept of Bayesian statistics
- Distributions often used in Bayesian statistics
- Prior distributions, likelihood functions, and posterior distributions
- Bayesian estimation and model fitting using appropriate software
- Differences between Bayesian estimation and maximum likelihood estimation
- Choice of prior distribution
- Empirical Bayes estimation
- Bayesian hierarchical models
- Empirical applications of Bayesian analysis using appropriate software
You must have successfully completed the following subject(s) before starting this subject:
SWI-STA60003-Basic Statistical Computing Using R, or SWI-HMS772 ;
Special requirements
No special requirements
The subject introduces the fundamentals of Bayesian statistical modelling. Students will learn the importance of subjective beliefs in Bayesian statistics. Important concepts such as prior distributions, likelihood functions, and posterior distributions will be discussed at length. Numerical estimation techniques, such as Metropolis-Hastings and Gibbs sampling, will be introduced. Empirical applications of Bayesian analysis will be performed in an R software environment.
Please note: assessment values are indicative only, details will be advised at the start of the subject.
- Assignments — 2 (40%)
- Invigilated Exam (50%)
- Quizzes — Online (10%)