No offerings have been identified for this subject in 2016

STA526 Stochastic Models (8)


This subject concentrates on problems where the probable or stochastic element is of paramount importance. Apart from determining what can be said under such conditions, the question of how this information can be used in problem solving is investigated. Empirical verification via computer simulation is used as part of the learning strategy.

+ Subject Availability Modes and Location

Continuing students should consult the SAL for current offering details prior to contacting their course coordinator: STA526
Where differences exist between the handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

Subject information

Duration Grading System School:
One sessionHD/FLSchool of Science and Technology

Assumed Knowledge


Learning Outcomes

Upon successful completion of this subject, students should:
Have developed an understanding of the basic theory underlying the definition, role and applications of stochastic processes;
Be able to emphasise the necessity of the stochastic approach via applications to practical problems;
Be able to show the connection bwtween the theory of stochastic processes and the use of simulation, especially discrete simulation.


The subject will cover the following topics:
Introduction to stochastic models; Generating functions; Random walks; Markov chains; Branching processes; Exponential distribution and poisson process; Birth and death processes; Queuing processes.


The information contained in the 2016 CSU Handbook was accurate at the date of publication: 06 September 2016. The University reserves the right to vary the information at any time without notice.