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 Outlines
Current CSU students can view Subject Outlines for recent sessions. Please note that Subject Outlines and assessment tasks are updated each session.

No offerings have been identified for this subject in 2018.

Where differences exist between the Handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

Subject Information

Grading System



One session


School 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.


This 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.


Current Students

For any enquiries about subject selection or course structure please contact Student Central or or phone on 1800 275 278.

Prospective Students

For further information about Charles Sturt University, or this course offering, please contact info.csu on 1800 275 278 (free call within Australia) or enquire online.

The information contained in the 2018 CSU Handbook was accurate at the date of publication: August 2018. The University reserves the right to vary the information at any time without notice.