Course Title: Stochastic Processes and Applications

Part A: Course Overview

Course Title: Stochastic Processes and Applications

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH1317

City Campus

Postgraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

 Sem 2 2006,
Sem 1 2010,
Sem 2 2013,
Sem 1 2015

MATH1317

City Campus

Postgraduate

171H School of Science

Face-to-Face

 Sem 1 2020,
Sem 2 2022

Course Coordinator: Dr Xu Zhang

Course Coordinator Phone: +61 9925 2000

Course Coordinator Email: xu.zhang@rmit.edu.au

Course Coordinator Availability: By appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

Assumed Knowledge

Basic knowledge of mathematical modelling and probability theory.


Course Description

Stochastic models are among the most widely used tools in operations research and management science. Stochastic processes and applications can be used to analyse and solve a diverse range of problems arising in production & inventory control, resource planning, service systems, computer networks and many others. This course, with an emphasis on model building, covers inventory models, Markov chains, Poisson processes and queuing theory.


Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for MC004 Master of Statistics and Operations Research and MC242 Master of Analytics:

Knowledge and technical competence

  • an understanding of appropriate and relevant, fundamental and applied mathematical knowledge, methodologies and modern computational tools.

Problem-solving

  • the ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • an understanding of the relationship between the purpose of a model and the appropriate level of complexity and accuracy.

Information literacy

  • the ability to locate and use data and information and evaluate its quality with respect to its authority and relevance.


On completion of this course you should be able to:

  1. Elucidate the power of stochastic processes and their range of applications;
  2. Demonstrate essential stochastic modelling tools including Markov chains and queuing theory;
  3. Formulate and solve problems which involve setting up stochastic models


Overview of Learning Activities

You will be actively engaged in a range of learning activities such as lectorials, tutorials, practicals, laboratories, seminars, project work, class discussion, individual and group activities. Delivery may be face to face, online or a mix of both. 

You are encouraged to be proactive and self-directed in your learning, asking questions of your lecturer and/or peers and seeking out information as required, especially from the numerous sources available through the RMIT library, and through links and material specific to this course that is available through myRMIT Studies Course


Overview of Learning Resources

RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course. 

There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal.


Overview of Assessment

Assessment Tasks:

Assessment Task 1:  Class Exercises
Weighting 48%
This assessment task supports CLOs 1 & 2

Assessment Task 2: Formative Assessment
Weighting 22%
This assessment task supports CLO 1, 2 & 3

Assessment Task 3: Final Timed Assessment
Weighting 30% 
This assessment supports CLO 1, 2 & 3

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.