Handbook home
Advanced Statistical Modelling (MAST90111)
Graduate courseworkPoints: 12.5On Campus (Parkville)
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Overview
Availability | Semester 2 |
---|---|
Fees | Look up fees |
Complex data consisting of dependent measurements collected at different times and locations are increasingly important in a wide range of disciplines, including environmental sciences, biomedical sciences, engineering and economics. This subject will introduce you to advanced statistical methods and probability models that have been developed to address complex data structures, such as functional data, geo-statistical data, lattice data, and point process data. A unifying theme of this subject will be the development of inference, classification and prediction methods able to cope with the dependencies that often arise in these data.
Intended learning outcomes
After completing this subject students should gain:
- An appreciation of the range and utility of advanced statistical models and a sound knowledge of their analysis using modern statistical methods.
- An appreciation of the computational methods required to fit these models and the ability to interpret the results of an analysis.
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;
- time-management skills: the ability to meet regular deadlines while balancing competing commitments;
- computer skills: the ability to use statistical computing packages.
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST90082 | Mathematical Statistics | Semester 1 (On Campus - Parkville) |
12.5 |
Plus one of:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST30025 | Linear Statistical Models | Semester 1 (On Campus - Parkville) |
12.5 |
MAST90104 | A First Course In Statistical Learning | Semester 2 (On Campus - Parkville) |
25 |
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
Any of the following:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST90085 | Multivariate Statistical Techniques | Semester 2 (On Campus - Parkville) |
12.5 |
MAST90083 | Computational Statistics and Data Mining | Semester 2 (On Campus - Parkville) |
12.5 |
MAST90084 | Statistical Modelling | Semester 1 (On Campus - Parkville) |
12.5 |
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: 3 November 2022
Assessment
Additional details
- Two written assignments amounting up to 50 pages, mid and late semester (20%)
- 3 hour written examination, held during examination period (60%)
- Project of 1,500 words, due during end of semester (20%)
Last updated: 3 November 2022
Dates & times
- Semester 2
Coordinator Liuhua Peng Mode of delivery On Campus (Parkville) Contact hours Total time commitment 170 hours Teaching period 23 July 2018 to 21 October 2018 Last self-enrol date 3 August 2018 Census date 31 August 2018 Last date to withdraw without fail 21 September 2018 Assessment period ends 16 November 2018
Time commitment details
170 hours
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Science (Mathematics and Statistics) Course Master of Data Science - 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.
Last updated: 3 November 2022