Course overview
This course introduces students to the fundamental concepts of stochastic processes, particularly Markov chains, and related structures. These time-dependent probabilistic models are essential for modelling many real-world systems, be it a telecommunications network, a hospital waiting list or a transport system. They also arise in many other environments, where you wish to capture the development of some element of random behaviour over time, such as the state of a game or a decision process. Stochastic processes also form the basis of many advanced numerical methods in applied mathematics and machine learning.
Course learning outcomes
- Demonstrate understanding of the mathematical basis of Markov chains in both discrete and continuous time
- Formulate stochastic models from verbal descriptions by making appropriate simplifying assumptions
- Demonstrate understanding of the mathematical basis of renewal processes
- Articulate the role of stochastic processes in systems modelling
- Write computer code to simulate different types of stochastic processes
Availability
Class details
Adelaide City Campus East
Class number 51411
Section FR01
Size 50
Available 50
Class number 51412
Section WS01
Size 50
Available 50
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