Stochastic Processes

Undergraduate | 2026

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Area/Catalogue
STAT X303
Course ID icon
Course ID
208192
Level of study
Level of study
Undergraduate
Unit value icon
Unit value
6
Course level icon
Course level
3
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Inbound study abroad and exchange
Inbound study abroad and exchange
The fee you pay will depend on the number and type of courses you study.
Yes
University-wide elective icon
University-wide elective course
Yes
Single course enrollment
Single course enrolment
Yes
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Note:
Course data is interim and subject to change

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

Prerequisite(s)

N/A

Corequisite(s)

N/A

Antirequisite(s)

N/A