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Bayesian Statistical Learning (MAST90125)
Graduate courseworkPoints: 12.5Dual-Delivery (Parkville)
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About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 2
Overview
Availability | Semester 2 - Dual-Delivery |
---|---|
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: 31 January 2024
Eligibility and requirements
Prerequisites
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST20005 | Statistics |
Summer Term (Dual-Delivery - Parkville)
Semester 2 (Dual-Delivery - 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 (Dual-Delivery - Parkville) |
12.5 |
MAST30025 | Linear Statistical Models | Semester 1 (Dual-Delivery - Parkville) |
12.5 |
MAST30027 | Modern Applied Statistics | Semester 2 (Dual-Delivery - Parkville) |
12.5 |
MAST30020 | Probability for Inference | Semester 1 (Dual-Delivery - 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: 31 January 2024
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: 31 January 2024
Dates & times
- Semester 2
Coordinator Feng Liu Mode of delivery Dual-Delivery (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 25 July 2022 to 23 October 2022 Last self-enrol date 5 August 2022 Census date 31 August 2022 Last date to withdraw without fail 23 September 2022 Assessment period ends 18 November 2022 Semester 2 contact information
Additional delivery details
This Dual-Delivery subject has On Campus assessment components.
Last updated: 31 January 2024
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
- 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.
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
This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.
Last updated: 31 January 2024