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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2013/2014
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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Stochastic Simulation (Level 10) (INFR10047)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate) Credits10
Home subject areaInformatics Other subject areaNone
Course website http://www.inf.ed.ac.uk/teaching/courses/ Taught in Gaelic?No
Course descriptionThis course teaches various aspects of simulation. Techniques of discrete-event, stochastic and continuous simulation are introduced. Examples are drawn from a range of application areas including computer systems but also chemical reactions and biology.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Students MUST NOT also be taking Stochastic Simulation (Level 11) (INFR11081)
Other requirements Successful completion of Year 3 of an Informatics Single or Combined Honours Degree, or equivalent by permission of the School. The only formal pre-requisite is a second level Mathematics course providing knowledge of elementary continuous mathematics.
Additional Costs None
Information for Visiting Students
Pre-requisitesNone
Displayed in Visiting Students Prospectus?Yes
Course Delivery Information
Not being delivered
Summary of Intended Learning Outcomes
1 - Students will understand the principal kinds of simulation methods and be able to choose the methods which are most appropriate for the current simulation study.
2 - Students will learn the difference between discrete-event simulation with an event list and discrete-state stochastic simulation. Students will learn how simulation of continuous-state systems differs from simulation of discrete-state systems.
3 - Students will learn how to draw well-justified conclusions from a set of simulation experiments. They will develop an understanding of the role of elementary statistical methods in making conclusions from simulation results.
4 - Students will gain experience in working with simulation toolkits coded in Java. These will include discrete-event simulation packages such as SSJ and stochastic simulation packages such as Dizzy.
5 - The case study work within the course allows the students to plan and carry out a set of simulation experiments and combine the results in a sound way.
6 - Students will develop an appreciation of random number generation, random variates, seeds and confidence intervals.
Assessment Information
Written Examination 75
Assessed Assignments 25
Oral Presentations 0

Assessment
The coursework is comprised of two practical exercises in which Java-based simulation packages are used. Knowledge of the Java programming language is assumed.
Special Arrangements
None
Additional Information
Academic description Not entered
Syllabus Not entered
Transferable skills Not entered
Reading list Not entered
Study Abroad Not entered
Study Pattern Not entered
KeywordsNot entered
Contacts
Course organiserDr Amos Storkey
Tel: (0131 6)51 1208
Email: A.Storkey@ed.ac.uk
Course secretaryMiss Kate Weston
Tel: (0131 6)50 2692
Email: Kate.Weston@ed.ac.uk
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