BEX5410 Bayesian inference and data analysis
Faculty of Business and Economics
BEX5410 Bayesian inference and data analysis is a level 5, 0-credit-point, postgraduate unit from the Faculty of Business and Economics, offered in 2023 in Semester 1 at Clayton. It has no prerequisites.
- Credit points
- 0
- Offered in 2023
- Semester 1
- Clayton
- Assessment
- Exam 45%
- and 1 other task
- Workload
- 144 hours
- per semester
The 2027 handbook has no page for BEX5410. This is its 2023 entry, the latest one.
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Requisites
Before BEX5410
Prohibitions
You can't enrol if you have passed any of these.
After BEX5410
No unit lists BEX5410 as a prerequisite in the 2023 handbook.
Enrolment rules
You must be enrolled in course 0029 or 3194 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 econometrics 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 2023
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
Assessment
- Within semester assessment55%
- Examination45%
Learning outcomes
When you finish this unit, you should be able to:
- 1
appreciate the importance of Bayesian statistical techniques in econometric research 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 econometric 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 approachProblem-based learning
- Teaching approachActive 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. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning, peer directed learning and online engagement.
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.
This unit engages you in actively applying your knowledge, skills and attributes in interactive, collaborative and reflective activities.
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.
Contacts
- Chief Examiners
- Associate Professor Catherine Forbes
Common questions
What are the prerequisites for BEX5410?
BEX5410 has no prerequisites, but enrolment rules apply.
When is BEX5410 offered?
In 2023, BEX5410 runs in Semester 1 at Clayton.
How much work is BEX5410?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does BEX5410 have an exam?
Yes. The exam is worth 45% of the final mark, alongside 1 other task.