APP MTH 7056 - Random Processes
North Terrace Campus - Semester 2 - 2014
-
General Course Information
Course Details
Course Code APP MTH 7056 Course Random Processes Coordinating Unit Applied Mathematics Term Semester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Prerequisites MATHS 1012 Assumed Knowledge Knowledge of Markov chains, such as would be obtained from MATHS 2103 Assessment ongoing assessment 30%, exam 70% Course Staff
No information currently available.
Course Timetable
The full timetable of all activities for this course can be accessed from .
-
Learning Outcomes
Course Learning Outcomes
1. 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最新糖心Vlog Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
最新糖心Vlog Graduate Attribute Course Learning Outcome(s) Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. 1,2,3,4 The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. 2,3,5 An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 2,3,4,5 Skills of a high order in interpersonal understanding, teamwork and communication. 5 A proficiency in the appropriate use of contemporary technologies. 3 A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. 1,2,3,4,5 An awareness of ethical, social and cultural issues within a global context and their importance in the exercise of professional skills and responsibilities. 3,4,5 -
Learning Resources
Required Resources
None.Recommended Resources
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, 2006Online Learning
Assignments, 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 Modes
The 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.Workload
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 Summary
Lecture Schedule
Week 1 Introduction and Review of Discrete-time Markov chains
Week 2 Modelling
Understanding the formulation of CTMCs
Week 3 Transient Behaviour
Kolmogorov DEs
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. -
Assessment
The 最新糖心Vlog'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 Summary
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 Requirements
An aggregate score of 50% is required to pass the course.Assessment Detail
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% Submission
Homework 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.Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending 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 .
-
Student Feedback
The 最新糖心Vlog 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 最新糖心Vlog 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.
-
Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
-
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
-
Fraud Awareness
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鈥檚 disciplinary procedures.
The 最新糖心Vlog of Adelaide is committed to regular reviews of the courses and programs it offers to students. The 最新糖心Vlog of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.
