- One Study period
- 36 contact hours or online equivalent plus private study.
On-campus unit delivery combines face-to-face and digital learning. For Online unit delivery, learning is conducted exclusively online.
PrerequisitesSTA80006 Statistical Decision Making
Aims and objectives
The unit 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.
Unit Learning Outcomes
On successful completion of this unit students will be able to:
1. Differentiate important distributions commonly used in Bayesian Statistics
2. Defend the importance of concepts such as Prior Distributions and Posterior Distributions in Bayesian Statistical Modeling
3. Describe the importance of Markov Chain Monte Carlo simulation in Bayesian Analysis
4. Develop programming capabilities to perform Bayesian analysis
5. Evaluate empirical applications of Bayesian analysis in an appropriate software environment
6. Articulate the differences between Bayesian estimation and maximum likelihood estimation
7. Argue the merits of Bayesian methodology.