Undergraduate Course: Stochastic Simulation (Level 10) (INFR10047)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Available to all students |
| Credit level (Normal year taken) | SCQF Level 10 (Year 4 Undergraduate) |
Credits | 10 |
| Home subject area | Informatics |
Other subject area | None |
| Course website |
http://www.inf.ed.ac.uk/teaching/courses/ |
Taught in Gaelic? | No |
| Course description | This 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 |
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Co-requisites | |
| Prohibited Combinations | Students MUST NOT also be taking
Stochastic Simulation (Level 11) (INFR11081)
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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-requisites | None |
| 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 |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Amos Storkey
Tel: (0131 6)51 1208
Email: A.Storkey@ed.ac.uk |
Course secretary | Miss Kate Weston
Tel: (0131 6)50 2701
Email: Kate.Weston@ed.ac.uk |
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