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MATH40480

Academic Year 2024/2025

Probability Theory (MATH40480)

Subject:
Mathematics
College:
Science
School:
Mathematics & Statistics
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Professor Neil O'Connell
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

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.

About this Module

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
Lectures

36
Autonomous Student Learning

72
Total

108

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 Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): Final exam End of trimester
Duration:
2 hr(s)
 Standard conversion grade scale 40% No
85
 No
Exam (In-person): Midterm Week 7 Standard conversion grade scale 40% No
15
 No

Carry forward of passed components
No
 

Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
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.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Fri 13:00 - 14:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Mon 13:00 - 13:50
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