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MATH3801 is a Mathematics Level III course. See the course overview below.

Units of credit:Ìý6

Prerequisites: (MATH2501 or MATH2601) and (MATH2011 or MATH2111) and (MATH2801 or MATH2901)

Exclusions:Ìý²Ñ´¡°Õ±á3901

Cycle of offering: Term 1

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

More information:

The course handout contains 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 .

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 contains up-to-date timetabling information.

If you are currently enrolled in MATH3801, you can log into  for this course.

This course is an introduction to the theory of stochastic processes. Informally, a stochastic process is a random quantity that evolves over time, like a gambler's net fortune and the price fluctuations of a stock on any stock exchange, for instance.

The main aims of this course are: 1) to provide a thorough but straightforward account of basic probability theory; 2) to introduce basic ideas and tools of the theory of stochastic processes; and 3) to discuss in depth through many examples important stochastic processes, including 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.

Introduction to stochastic processes, that is, processes that evolve over time such as price fluctuations of a stock. The course emphasises theory and applications, and covers discrete- and continuous-time Markov chains, Poisson processes and Brownian motion.