MATH40480 Probability Theory

Academic Year 2023/2024

Probability theory has its roots in games of chance, such as coin tosses or throwing dice. By playing these games, one develops some probabilistic intuition. Such intuition guided the early development of probability theory and allowed for rigorous mathematical statements to be made concerning such games as well as more complex problems involving randomness.

In this course, we will develop the mathematical tools required for the study of randomness. Measure theory and integration play a fundamental role in this development, as does independence and various notions of convergence of random variables. Having covered the basics of probability and measure, we will learn about (and prove in some cases) some of the most essential results in probability theory such as laws of large numbers, Borel-Cantelli lemmas, and central limit theorem. We will also introduce and study conditional expectations, martingales and Brownian motion.

Show/hide contentOpenClose All

Curricular information is subject to change

Learning Outcomes:

On the completion of this module the student should be familiar with the fundamental concepts of probability theory. This includes probability measures, random variables, independence, expectation, modes of convergence, laws of large numbers, central limit theorem, conditional expectation, martingales, Brownian motion. The student will develop their ability to deal with abstract concepts and to relate them to concrete examples. The student's ability to realise and critique proofs and arguments will be enhanced.

Indicative Module Content:

Probability and measure; laws of large numbers; central limit theorem; conditional expectation; martingales; introduction to Brownian motion.

Student Effort Hours: 
Student Effort Type Hours


Autonomous Student Learning




Approaches to Teaching and Learning:
Lectures, tutorials, enquiry and problem-based learning. 
Requirements, Exclusions and Recommendations
Learning Recommendations:

Students are strongly recommended to revise Introduction to Probability (STAT20110) and Measure Theory & Integration (MATH30360) prior to commencing the course.

Module Requisites and Incompatibles

Additional Information:
Students must have completed MATH30360 Measure Theory and Integration as a pre requisite for this module.

Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Class Test: In-class test Week 7 n/a Standard conversion grade scale 40% No


Examination: 2 hour exam. 2 hour End of Trimester Exam No Standard conversion grade scale 40% No



Carry forward of passed components
Resit In Terminal Exam
Autumn Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.