Advanced Statistical Modelling (MAST90111)

Graduate courseworkPoints: 12.5Not available in 2025

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Overview

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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: 4 March 2025

Eligibility and requirements

Prerequisites

Code Name Teaching period Credit Points
MAST90082 Mathematical Statistics
Semester 1 (On Campus - Parkville)
 12.5

AND

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
No longer available
MAST90083 Computational Statistics & Data Science
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: 4 March 2025

Assessment

DescriptionTimingPercentage

Two written assignments amounting up to 50 pages, mid and late semester

  • 50 pages
Second half of the teaching period20%

Written examination

  • 3 hours
During the examination period60%

Project

  • 1500 words
End of the teaching period20%

Last updated: 4 March 2025

Dates & times

Not available in 2025

Time commitment details

170 hours

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.

Last updated: 4 March 2025

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:

    TypeName
    CourseMaster of Science (Mathematics and Statistics)
    CourseMaster 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.

    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

    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: 4 March 2025