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

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

 

The objectives of this course are best learnt through lecture recordings supported by lectorial and class exercises. After a brief review of probability theory and mathematical modelling, the topics of decision making under uncertainty, inventory models, Poisson processes, Markov chains and queuing theory will be covered in detail during lecture recordings. While attendance at lectorials is not compulsory, you will find that regular attendance is necessary as lectorials will be important aspects of the learning experience.

The course is supported by the Canvas learning system. Assessment comprises class exercises, a formative assessment and a final timed assessment. 

 


Overview of Learning Resources

 

Some basic lecture notes for this course will be available on Canvas. A recommended reading list will also be provided.

Library Subject Guide for Mathematics & Statistics http://rmit.libguides.com/mathstats


Overview of Assessment

Note that:

☒This course has no hurdle requirements.

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