STATS 320 : Applied Stochastic Modelling


2020 Semester One (1203) (15 POINTS)

Course Prescription

Introduction to stochastic modelling, with an emphasis on queues and models used in finance. Behaviour of Poisson processes, queues and continuous time Markov chains will be investigated using theory and simulation.

Course Overview

This course will provide an introduction to stochastic modelling. Various applications and models related to biology, physics, computer science, engineering, economics, business and operations research will be investigated using both theory and simulation. Topics include: Introductory probability theory, discrete time Markov chains, Poisson processes, birth and death processes, continuous time Markov chains, queueing theory, computer simulation of stochastic models. Other topics or applications in stochastic modelling, such as: Google's PageRank algorithm, Ehrenfest's diffusion model, population genetics, Wright-Fisher model, Moran model, coalescence processes, Metropolis-Hasting algorithm, Markov Chain Monte Carlo (MCMC) simulation, telecommunication networks, network routing, Jackson networks, priority queues, optimization in modelling, branching processes, migration networks, reservoir models.

Course Requirements

Prerequisite: 15 points from STATS 125, 210, 225 and 15 points from STATS 201, 207, 208, 220, BIOSCI 209

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Graduate Profile: Bachelor of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Demonstrate a good knowledge and use of the main results and techniques of stochastic modelling (Capability 1, 3 and 4)
  2. Work effectively with probabilities, expectations, and random variables (Capability 1, 3 and 4)
  3. Solve a variety of problems involving Markov chains, Poisson processes and queueing theory (Capability 1, 2, 3 and 4)
  4. Use stochastic modelling to analyse and explain the behaviour in a variety of applications (Capability 1, 2, 3 and 4)
  5. Demonstrate an ability to use and interpret computer simulation in stochastic modelling (Capability 1, 2, 3 and 4)


Assessment Type Percentage Classification
Assignments 30% Individual Coursework
Test 10% Individual Test
Exam 60% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5

Students must obtain at least 50% in the exam in order to pass the course.

Learning Resources

Course notes will be posted to Canvas and distributed in lectures.
There is no course book, but a reading list will be available for students who wish to consult it.

Special Requirements


Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, each week you can typically expect 3 hours of lectures, a 1 hour lab/tutorial, 2.5 hours of reading and thinking about the content, and 3.5 hours of work on assignments and/or test preparation.

Digital Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website at

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page:

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter (


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 11/01/2020 03:23 p.m.