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:
- Elucidate the power of stochastic processes and their range of applications;
- Demonstrate essential stochastic modelling tools including Markov chains and queuing theory;
- 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