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Stochastic Modelling (MAST30001)
Undergraduate level 3Points: 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 |
Stochastic processes occur in finance as models for asset prices, in telecommunications as models for data traffic, in computational biology as hidden Markov models for gene structure, in chemistry as models for reactions, in manufacturing as models for assembly and inventory processes, in biology as models for the growth and dispersion of plant and animal populations, in speech pathology and speech recognition and many other areas.
This course introduces the theory of stochastic processes including Poisson processes, Markov chains in discrete and continuous time, and renewal processes. These processes are illustrated using examples from real-life situations. It then considers in more detail important applications in areas such as queues and networks (the foundation of telecommunication models), finance, and genetics.
Intended learning outcomes
After completing this subject students should:
- understand the basic concepts of random processes in discrete and continuous time;
- acquire an appreciation of how randomness and variability in time can be mathematically described using probability theory;
- be able to build, analyze and simulate basic stochastic models for simple real-life random phenomena evolving in time.
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: 27 April 2024
Eligibility and requirements
Prerequisites
One of
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST20026 | Real Analysis |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
MAST10009 | Accelerated Mathematics 2 | Semester 2 (On Campus - Parkville) |
12.5 |
and one of
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST20004 | Probability | Semester 1 (On Campus - Parkville) |
12.5 |
MAST20006 | Probability for Statistics | 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: 27 April 2024
Assessment
Additional details
Two written assignments due mid-semester and at the end of semester amounting to a total of up to 50 pages (20%), and a 3-hour written examination in the examination period (80%).
Last updated: 27 April 2024
Dates & times
- Semester 2
Principal coordinator Mark Holmes Mode of delivery On Campus (Parkville) Contact hours 3 x one hour lectures per week, 1 x one hour practice class per week Total time commitment 170 hours Teaching period 24 July 2017 to 22 October 2017 Last self-enrol date 4 August 2017 Census date 31 August 2017 Last date to withdraw without fail 22 September 2017 Assessment period ends 17 November 2017
Time commitment details
Estimated total time commitment of 170 hours
Last updated: 27 April 2024
Further information
- Texts
Prescribed texts
K. Borovkov, Elements of Stochastic Modelling. World Scientific, Singapore, 2003.
- Subject notes
This subject is available for science credit to students enrolled in the BSc (both pre-2008 and new degrees), BASc or a combined BSc course.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Commerce (Finance) Course Master of Commerce (Finance) Informal specialisation Applied Mathematics Informal specialisation Statistics / Stochastic Processes Informal specialisation Applied Mathematics Informal specialisation Science-credited subjects - new generation B-SCI and B-ENG. Informal specialisation Selective subjects for B-BMED Informal specialisation Statistics / Stochastic Processes Major Statistics / Stochastic Processes Informal specialisation Statistics / Stochastic Processes Informal specialisation Applied Mathematics Major Applied Mathematics Informal specialisation Operations Research / Discrete Mathematics - Breadth options
This subject is available as breadth in the following courses:
- 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: 27 April 2024