APP MTH 4116 - Random Processes - Honours
North Terrace Campus - Semester 2 - 2017
General Course Information
Course Code APP MTH 4116 Course Random Processes - Honours Coordinating Unit School of Mathematical Sciences Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Prerequisites MATHS 1012 Assumed Knowledge Knowledge of Markov chains, such as would be obtained from MATHS 2103 Course Description This course introduces students to the fundamental concepts of random processes, particularly continuous-time Markov chains, and related structures. These are the essential building blocks of any random system, be it a telecommunications network, a hospital waiting list or a transport system. They also arise in many other environments, where you wish to capture the development of some element of random behaviour over time, such as the state of the surrounding environment.
Topics covered are: Continuous-time Markov-chains: definition and basic properties, transient behaviour, the stationary distribution, hitting probabilities and expected hitting times, reversibility; Queueing Networks: Kendall's notation, Jackson networks, mean; Loss Networks: truncated reversible processes, circuit-switched networks, reduced load approximations. Basic Queueing Theory: arrival processes, service time distributions, Little's Law; Point Processes: Poisson process, properties and generalisations; Renewal Processes: preliminaries, renewal function, renewal theory and applications, stationary and delayed renewal processes.
Course Coordinator: Dr Andrew Smith
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes1. demonstrate understanding of the mathematical basis of continuous-time Markov chains
2. demonstrate the ability to formulate continuous-time Markov chain models for relevant practical systems
3. demonstrate the ability to apply the theory developed in the course to problems of an appropriate level of difficulty
4. develop an appreciation of the role of random processes in system modelling
5. demonstrate skills in communicating mathematics orally and in writing
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
all Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
all Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
all Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
1,3 Intercultural and ethical competency
- adept at operating in other cultures
- comfortable with different nationalities and social contexts
- able to determine and contribute to desirable social outcomes
- demonstrated by study abroad or with an understanding of indigenous knowledges
all Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
Students may wish to consult any of the following books, available in the Library.
1. Introduction to Probability Models, (currently the 10th edition), Sheldon Ross, Academic Press, 2009
2. Introduction to Stochastic Models (2nd edition), R. Goodman, Dover Publications, 2006
Online LearningAssignments, tutorial exercises, handouts, video recordings of lectures and course announcements will be posted on MyUni.
Please don't hesitate to email the lecturer should anything be missing.
Learning & Teaching Activities
Learning & Teaching ModesThe lecturer guides the students through the course material in 30 lectures. Students are expected to engage with the material in the lectures. Interaction with the lecturer and discussion of any difficulties that arise during the lecture is encouraged. Students are
expected to attend all lectures, but lectures will be recorded to help with occasional absences and for revision purposes. In fortnightly
tutorials students present their solutions to assigned exercises and discuss them with the lecturer and each other. Fortnightly homework
assignments help students strengthen their understanding of the theory and their skills in applying it, and allow them to gauge their progress.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload hours Lectures 30 90 Tutorials 5 20 Assignments 5 25 Project 1 20 Total 155
Learning Activities SummaryLecture Schedule
Week 1 Introduction and Review of Discrete-time Markov chains
Week 2 Modelling
Understanding the formulation of CTMCs
Week 3 Transient Behaviour
Week 4 Equilibrium Behaviour
Global Balance equations and characterisation
Week 5 Hitting Times and Reversibility
Hitting probabilities, expected hitting times and reversible Markov chains
Week 6 Queueing Networks
Burke’s Theorem, Jackson Networks, the theory of truncation of reversible Markov chains and application to queueing networks
Week 7 Reduced Load Approximations
Erlang Fixed Point Method
Week 8 Observed distributions
PASTA, Waiting time distributions, Little’s Law, Pollaczek-Khinchin
Week 9 Point processes
Background and Markovian Arrival Processes
Week 10 Renewal Theory
Riemann-Stieltjes Integration, Laplace Stieltjes Transform, the Convolution Theorem
Week 11 Renewal Theory
Convergence of random variables, the counting and waiting time processes, the renewal function
Week 12 Renewal Theory
Generalised renewal equation, the Basic, Blackwell’s and Elementrary Renewal Theorems, forward and backward recurrence times, Delayed and Stationary renewal processes
Tutorials in Weeks 3, 5, 7, 9, 11 cover the material of the previous few weeks.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Task Task type Due Weighting Learning outcomes Examination Summative Examination period 70% All Homework assignments Formative and summative Weeks 2, 4, 6, 8, 12 15% All Project Formative and summative Week 10 15% All
Assessment Related RequirementsAn aggregate score of 50% is required to pass the course.
Assessment task Set Due Weighting Assignment 1 Week 1 Week 2 3% Assignment 2 Week 3 Week 4 3% Assignment 3 Week 5 Week 6 3% Assignment 4 Week 7 Week 8 3% Assignment 5 Week 11 Week 12 3% Project Week 3 Week 10 15%
SubmissionHomework assignments must be submitted on time with a signed assessment cover sheet. Late assignments will not be accepted. Assignments will be returned within two weeks. Students may be excused from an assignment for medical or compassionate reasons. Documentation is required and the lecturer must be notified as soon as possible.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M11 (Honours Mark Scheme) Grade Grade reflects following criteria for allocation of grade Reported on Official Transcript Fail A mark between 1-49 F Third Class A mark between 50-59 3 Second Class Div B A mark between 60-69 2B Second Class Div A A mark between 70-79 2A First Class A mark between 80-100 1 Result Pending An interim result RP Continuing Continuing CN
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
- Academic Support with Maths
- Academic Support with writing and speaking skills
- Student Life Counselling Support - Personal counselling for issues affecting study
- International Student Support
- AUU Student Care - Advocacy, confidential counselling, welfare support and advice
- Students with a Disability - Alternative academic arrangements
- Reasonable Adjustments to Teaching & Assessment for Students with a Disability Policy
- LinkedIn Learning
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangement Policy
- Academic Honesty Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Elder Conservatorium of Music Noise Management Plan
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.
The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.