Bayesian Statistical Learning (MAST90125)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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As part of the University’s response to COVID-19 and the associated Government restrictions and guidelines, most subjects will continue to be delivered online in Winter and Semester 2.
For information about the University’s phased return to campus and in-person activity in Winter and Semester 2, please refer to the on-campus subjects page.

Please refer to the LMS for up-to-date subject information, including assessment and participation requirements, for subjects being offered in 2020.

Overview

Availability
Semester 2
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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, comparisons of means and proportions, multi-model inference and model selection. The second part of the subject focuses on advanced supervised and unsupervised Bayesian machine learning methods in the context of Gaussian processes and Dirichlet processes. The subject will also cover practical implementations of Bayesian methods through Markov Chain Monte Carlo computing and real data applications.

Intended learning outcomes

LO1 A deep understanding of selected advanced topics in Bayesian statistics.


LO2 Development of the mathematical and computational skills needed for further research or applied work in statistics and data science.


LO3 Preparation for a research or industry career in statistics and data science.


LO4 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: 3 November 2022

Eligibility and requirements

Prerequisites

MAST20005 and any third-year subject in statistics or stochastic processes.

These may include: MAST30001 Stochastic Modelling, MAST30025 Linear Statistical models, MAST30027 Modern Applied Statistics, MAST30020 Probability for Inference

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: 3 November 2022

Assessment

Due to the impact of COVID-19, assessment may differ from that published in the Handbook. Students are reminded to check the subject assessment requirements published in the subject outline on the LMS

DescriptionTimingPercentage

Up to 60 pages of written assignments (three assignments worth 10% each) due in weeks 4, 8 and 12

  • 60 pages
From Week 4 to Week 1230%

Written examination

  • 3 hours
During the examination period70%

Last updated: 3 November 2022

Dates & times

  • Semester 2
    Principal coordinatorJohn Holmes
    Mode of deliveryOn Campus (Parkville)
    Contact hours2 x 1 hour lectures, 1 x 1 hour computer lab
    Total time commitment170 hours
    Teaching period 3 August 2020 to 1 November 2020
    Last self-enrol date14 August 2020
    Census date21 September 2020
    Last date to withdraw without fail16 October 2020
    Assessment period ends27 November 2020

    Semester 2 contact information

    john.holmes@unimelb.edu.au

Last updated: 3 November 2022

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.

    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: 3 November 2022