Random Processes (MAST90019)

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
FeesLook up fees

The subject covers the key aspects of the theory of stochastic processes that plays the central role in modern probability and has numerous applications in natural sciences and industry. We discuss the following topics: ways to construct and specify random processes, discrete time martingales, Levy processes and more general continuous time Markov processes, point processes. Applications to modelling random phenomena evolving in time are discussed throughout the course.

Intended learning outcomes

After completing this subject students should:



  • gain an understanding of the basic concepts of the theory of stochastic processes;
  • gain an understanding of the fundamental techniques used in the study of random processes;
  • extend their ability to construct mathematical models for real-life situations involving uncertainty and evolving in time;
  • gain the ability to pursue further studies in this and related areas.

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

Both of the following, or equivalent.

Code Name Teaching period Credit Points
MAST30001 Stochastic Modelling
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: 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 40 pages of written assignments (two assignments worth 10% each, due mid and late in semester)

  • 40 pages
During the teaching period20%

A written examination

  • 3 hours
During the examination period80%

Last updated: 3 November 2022

Dates & times

  • Semester 2
    Principal coordinatorAihua Xia
    Mode of deliveryOn Campus (Parkville)
    Contact hours36 hours comprising one 2-hour lecture per week and one 1-hour practice class per week.
    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

    aihuaxia@unimelb.edu.au

Time commitment details

170 hours

Last updated: 3 November 2022

Further information

  • Texts

    Prescribed texts

    Recommended texts and other resources

    Grimmett, G.R. and Stirzaker, D.R., Probability and Random Processes. Clarendon Press, Oxford, 2001 (third edition).

  • Related Handbook entries

    This subject contributes to the following:

    TypeName
    CourseMaster of Data Science
    CourseMaster of Science (Mathematics and Statistics)
    CoursePh.D.- Engineering
    CourseDoctor of Philosophy - Engineering
    CourseMaster of Philosophy - Engineering
    Informal specialisationMathematics and Statistics
  • 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