ETC4541 Bayesian inference and data analysis
Faculty of Business and Economics
ETC4541 Bayesian inference and data analysis is a level 4, 6-credit-point, undergraduate and postgraduate unit from the Faculty of Business and Economics, offered in 2027 in Semester 1 at Clayton. It needs ETC3400, ETC2520, ETC3410, ETC3450, ETC3580 or ETF3600.
- Credit points
- 6
- Offered in 2027
- Semester 1
- Clayton
- Assessment
- Exam 45%
- and 2 other tasks
- Workload
- 144 hours
- per semester
Reviews
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Requisites
Before ETC4541
Prohibitions
You can't enrol if you have passed any of these.
Prerequisites
Pass these before you enrol.
- ETC3400Principles of econometricsNo reviews yet
- ETC2520Probability and statistical inference for economics and businessNo reviews yet
- ETC3410Applied econometricsNo reviews yet
- ETC3450Applied time series econometricsNo reviews yet
- ETC3580Advanced statistical modellingNo reviews yet
- ETF3600Quantitative analysis of limited dependent variablesNo reviews yet
After ETC4541
No unit lists ETC4541 as a prerequisite in the 2027 handbook.
Enrolment rules
You must be enrolled in course B3701 to undertake this unit.
Equivalent units
The same content under another code. Only one of them counts.
Overview
This unit introduces you to both foundational and methodological aspects of Bayesian inference and data analysis. Topics covered include a review of the philosophical and probabilistic foundations of Bayesian inference; the contrast between the Bayesian and frequentist (or classical) statistical paradigms; the use of prior information via the specification of objective, Jeffreys and subjective prior distributions; Bayesian linear regression; the use of simulation techniques in Bayesian inference, including Markov chain Monte Carlo algorithms; Bayesian analysis of Gaussian and non-Gaussian time series econometric models, including state space models; and the Kalman filter as a Bayesian updating rule.
Offerings in 2027
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
Assessment
- Exercise30%
- Project25%
- Examination45%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Learning outcomes
When you finish this unit, you should be able to:
- 1
appreciate the importance of Bayesian statistical techniques and understand the differences between the Bayesian and frequentist statistical paradigms
- 2
acquire the skills necessary to derive Bayesian results analytically, in simple models
- 3
demonstrate an understanding of simulation methods and be able to implement these methods in empirically realistic models for data analysis
- 4
understand the Kalman filter and its role in Bayesian inference in state space models.
Workload and teaching
- Workshops36 hours
- Teaching approachEnquiry-based learning
- Teaching approachActive learning
- Teaching approachResearch activities
- Teaching approachProblem-based learning
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.
This unit engages you in enquiry-based learning, where you will be encouraged to use your own knowledge to develop and engage in a process of enquiry, study and research to identify areas to be investigated and an approach to doing so.
This unit engages you in actively applying your knowledge, skills and attributes in interactive, collaborative and reflective activities.
This unit allows you to develop your research skills by engaging in structured inquiry using a systematic approach and discipline-specific methodologies.
This unit includes problem-based learning approaches, where you engage in research, integrate theory and practice and apply knowledge and skills to develop viable solutions in response to a problem or set of problems.
Learning resources
Technology resources
There may be an additional cost associated with purchasing a physical and/or virtual calculator. Specific details will be provided in the Learning Management System by commencement of Orientation week.
Where it fits
ETC4541 is part of 2 areas of study in the 2027 handbook.
Contacts
- Chief Examiners
- Professor Catherine Forbes
Common questions
What are the prerequisites for ETC4541?
You need ETC3400, ETC2520, ETC3410, ETC3450, ETC3580 or ETF3600 before you enrol. Enrolment rules also apply.
When is ETC4541 offered?
In 2027, ETC4541 runs in Semester 1 at Clayton.
How much work is ETC4541?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does ETC4541 have an exam?
Yes. The exam is worth 45% of the final mark, alongside 2 other tasks.
Which majors and minors include ETC4541?
ETC4541 is part of Business analytics and Econometrics.