MATH5835 is a honours and postgraduate coursework mathematics/statistics course.

Units of credit: 6

Pre-requisites: 24 units of level III mathematics (including 6 units in MATH3801 Probability and Stochastic Processes or MATH3901 Higher Probability and Stochastic Processes or an equivalent course) or a degree in a numerate discipline or permission of course authority.

Cycle of offering: Term 1 

Graduate attributes: The course will enhance your research, inquiry and analytical thinking abilities.

More information: The course outline for 2023 will be linked within the course offering table for the term applicable. Outlines will be made available prior to the start of the applicable term. 

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 MATH5835, you can log into UNSW Moodle for this course.

Course overview

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. This course introduces some of the basic ideas and tools to study such phenomena. In particular, we will introduce a concept of martingale to study phenomena evolving in discrete time and the concept of Poisson process (and its generalizations) and Brownian Motion to study processes evolving continuously in time. Some applications to statistical inference will also be discussed.