MATH5901 is a Honours and Postgraduate Coursework Mathematics course. See the course overview below

Units of credit: 6

Prerequisites: (MATH2501 or MATH2601) and (MATH2011 or MATH2111) and (MATH2801 or MATH2901), or admitted to a Postgraduate Mathematics or Statistics program.

Exclusion: MATH3801 or MATH3901 (jointly taught with MATH5901) 

Cycle of offering: T1 2023 

Graduate attributes: The course will enhance your research, inquiry and analytical thinking abilities. This course aims to introduce some of the basic ideas and tools of the theory of stochastic processes. The theory of stochastic processes deals with phenomena evolving randomly in time and/or space, such as prices on financial markets, air temperature or wind velocity, spread of diseases, number of hospital admissions in certain area, and many others.

More information: The Course outline will be made available closer to the start of term - please visit this website: www.unsw.edu.au/course-outlines

The Course Outline provides information about course objectives, assessment, course materials and the syllabus.

Important additional information as of 2023

UNSW Plagiarism Policy

The University requires all students to be aware of its policy on plagiarism.

For courses convened by the School of Mathematics and Statistics no assistance using generative AI software is allowed unless specifically referred to in the individual assessment tasks.

If its use is detected in the no assistance case, it will be regarded as serious academic misconduct and subject to the standard penalties, which may include 00FL, suspension and exclusion.

The online handbook entry contains information about the course. The timetable is only up-to-date if the course is being offered this year.

If you are currently enrolled in MATH5901, you can log into UNSW Moodle for this course.

Course overview

This course introduces some of the basic ideas and tools to study such phenomena. In particular, we will introduce Markov Chains (both in discrete and continuous time), Poisson processes, Brownian motion and Martingales.

The course will also cover other important but less routine topics, like Markov decision processes and some elements of queueing theory.