Postgraduate Course: Stochastic Simulation (Level 11) (INFR11081)
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 11 (Postgraduate) |
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.
This course is identical to the level 10 version except for the assessed coursework and additional learning outcome. |
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | Students MUST NOT also be taking
Stochastic Simulation (Level 10) (INFR10047)
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Other requirements | For Informatics PG and final year MInf students only, or by special 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.
7 - Students will demonstrate their knowledge of the state of the art of a topic area covered in the course. |
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. A substantial component of the assignment will be a literature review of recent developments and the state of the art in a topic area relevant to the course. |
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 Douglas Armstrong
Tel: (0131 6)50 4492
Email: Douglas.Armstrong@ed.ac.uk |
Course secretary | Mr Neil Mcgillivray
Tel: (0131 6)50 5160
Email: Neil.McGillivray@ed.ac.uk |
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