Postgraduate Course: Stochastic Simulation (Level 11) (INFR11081)
|School||School of Informatics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|Summary||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)
|Prohibited Combinations|| Students MUST NOT also be taking
Stochastic Simulation (Level 10) (INFR10047)
||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.
Information for Visiting Students
Course Delivery Information
|Not being delivered|
| 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.
|Course organiser||Dr Douglas Armstrong
Tel: (0131 6)50 4492
|Course secretary||Mr Neil Mcgillivray
Tel: (0131 6)50 5160