MATH3801 Probability and Stochastic Processes

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

Units of credit: 6

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

Exclusions: MATH3901

Cycle of offering: Term 1

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

More information:

This recent course handout (pdf) contains information about course objectives, assessment, course materials and the syllabus.

The Online Handbook entry contains up-to-date timetabling information.

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

Course Aims

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.

Course Description

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.