Bayesian Statistical Learning (MAST90125)
Graduate courseworkPoints: 12.5On Campus (Parkville)
Overview
| Availability | Semester 2 |
|---|---|
| Fees | Look up fees |
Bayesian inference treats all unknowns as random variables, and the core task is to update the probability distribution for each unknown as new data is observed. After introducing Bayes’ Theorem to transform prior probabilities into posterior probabilities, the first part of this subject introduces theory and methodological aspects underlying Bayesian statistical learning including credible regions, prior choice, comparisons of means and proportions, multi-model inference and model selection. The second part of the subject will cover practical implementations of Bayesian methods through Markov Chain Monte Carlo computing and real data applications, focusing on (generalised) linear models and concluding by exploring machine learning techniques such as Gaussian processes.
Intended learning outcomes
After completing this subject students should have:
- An understanding of selected advanced topics in Bayesian statistics;
- Developed the mathematical and computational skills needed for further research or applied work in statistics and data science;
- Preparation for a research or industry career in statistics and data science;
- Familiarity with several major texts in Bayesian statistics.
Generic skills
- In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include: - problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies; - analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis; - collaborative skills: the ability to work in a team; - time-management skills: the ability to meet regular deadlines while balancing competing commitments
Last updated: 4 March 2025
Eligibility and requirements
Prerequisites
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST20005 | Statistics |
Semester 2 (On Campus - Parkville)
Summer Term (On Campus - Parkville)
|
12.5 |
AND
12.5 credit points of level 3 subject in statistics or stochastic processes which could include one of the below:
One of
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST30001 | Stochastic Modelling | Semester 2 (On Campus - Parkville) |
12.5 |
| MAST30025 | Linear Statistical Models | Semester 1 (On Campus - Parkville) |
12.5 |
| MAST30027 | Modern Applied Statistics | Semester 2 (On Campus - Parkville) |
12.5 |
| MAST30020 | Probability for Inference | Semester 1 (On Campus - Parkville) |
12.5 |
Corequisites
None
Non-allowed subjects
None
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 4 March 2025
Assessment
| Description | Timing | Percentage |
|---|---|---|
Written assignment, up to 15 pages (approx. 10 hours time commttiment)
| Week 4 | 10% |
Written assignment, up to 20 pages (approx. 15 hours time commttiment)
| Week 9 | 15% |
Written assignment, up to 10 pages (approx. 5 hours time committment)
| Week 12 | 5% |
Written examination
| During the examination period | 70% |
Additional details
This Dual-Delivery subject has On Campus assessment components.
Last updated: 4 March 2025
Dates & times
- Semester 2
Coordinator Guoqi Qian Mode of delivery On Campus (Parkville) Contact hours Total of 36 contact hours: 2 x 1 hour lectures and 1 x 1 hour computer lab per week Total time commitment 170 hours Teaching period 28 July 2025 to 26 October 2025 Last self-enrol date 8 August 2025 Census date 1 September 2025 Last date to withdraw without fail 26 September 2025 Assessment period ends 21 November 2025 Semester 2 contact information
What do these dates mean
Visit this webpage to find out about these key dates, including how they impact on:
- Your tuition fees, academic transcript and statements.
- And for Commonwealth Supported students, your:
- Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.
Additional delivery details
This Dual-Delivery subject has On Campus assessment components.
Last updated: 4 March 2025
Further information
- Texts
- Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
- Available to Study Abroad and/or Study Exchange Students
Last updated: 4 March 2025