Course Title: Stochastic Processes and Applications
Part A: Course Overview
Course Title: Stochastic Processes and Applications
Credit Points: 12.00
Course Coordinator: Dr Xu Zhang
Course Coordinator Phone: +61 3 9925 2000
Course Coordinator Email: xu.zhang@rmit.edu.au
Course Coordinator Availability: by email
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
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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 lectures 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 lectures. While attendance at lectures is not compulsory, you will find that regular attendance is necessary as lectures will be important aspects of the learning experience.
The course is supported by the Blackboard learning system. Assessment comprises class exercises, a test and an exam.
Overview of Learning Resources
Some basic lecture notes for this course will be available on Blackboard. 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 25%
This assessment task supports CLOs 1,2 & 3
Assessment Task 2: Mid Semester Test
Weighting 25%
This assessment task supports CLO 1,2 &3
Assessment Task 3: Final Exam
Weighting 50%
This assessment supports CLO 1,2 & 3