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Bayesian Statistics
Postgraduate | SWI-STA80007 | 2019
Course information for 2019 intake
Calculate and apply your understanding of Bayesian statistical modelling. Figure out a sum of estimation techniques. Account for estimation differences. Assess the importance of Markov Chain Monte Carlo simulation in Bayesian analysis.
- Study method
- 100% online
- Assessments
- Subject may require attendance
- Entry requirements
- Part of a degree
- Duration
- -
FEE-HELP available
Bayesian Statistics
About this subject
Students who successfully complete this subject will be able to:
- Differentiate important distributions commonly used in Bayesian Statistic
- Defend the importance of concepts such as Prior Distributions and Posterior Distributions in Bayesian Statistical Modeling
- Describe the importance of Markov Chain Monte Carlo simulation in Bayesian Analysis
- Develop programming capabilities to perform Bayesian analysis
- Evaluate empirical applications of Bayesian analysis in an appropriate software environment
- Articulate the differences between Bayesian estimation and maximum likelihood estimation
- Argue the merits of Bayesian methodology.
- 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
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%)
For textbook details check your university's handbook, website or learning management system (LMS).
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Entry requirements
To enrol in this subject, you must be admitted into a degree.
Prior study
You must have successfully completed the following subject(s) before starting this subject:
one of
- SWI-STA60003-Basic Statistical Computing Using R
SWI-HMS772 (Not currently available)
Additional requirements
No additional requirements
Study load
- 0.125 EFTSL
- This is in the range of 10 to 12 hours of study each week.
Equivalent full time study load (EFTSL) is one way to calculate your study load. One (1.0) EFTSL is equivalent to a full-time study load for one year.
Find out more information on Commonwealth Loans to understand what this means to your eligibility for financial support.