Bayesian Statistics

STA80007 12.5 Credit Points Hawthorn, Online


  • One Study period

Contact hours

  • 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.

2022 teaching periods


HOL Study Period 3

29 Aug 22 - 27 Nov 22

23 Dec 22

Last self enrolment:
11 Sep 22

19 Sep 22

Last withdraw without fail:
14 Oct 22


STA80006 Con Current Requisite



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.
Students who successfully complete this unit 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.

Courses with unit

This unit will not be offered after Semester 2 2022