The course aims at providing the students with the fundamentals of experimental design techniques in Stochastic simulation. The focus is on telecom applications. After completion of the course the students should be able generate random variables of arbitrary distributions, make parameter estimates based on simulation results and assess their statistical error, to test hypotheses with simulations, to design simulations to lower the variance of usual simulation estimators, and finally, to determine whether the stochastic model chosen is consistent with a set of actual data
Course memo Autumn 2022
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Course presentation
Headings denoted with an asterisk ( * ) is retrieved from the course syllabus version Spring 2019
Content and learning outcomes
Course contents
1.Introduction & Probability review.
2.Random variable generators
3.Output data analysis: parameter estimation, correlation
4.Variance reduction techniques
5.Validation techniques & Hypothesis testing
Intended learning outcomes
The course aims at providing the students with the fundamentals of experimental design techniques in Stochastic simulation. The focus is on telecom applications. After completion of the course the students should be able to
- generate random variables of arbitrary distributions,
- make parameter estimates based on simulation results and assess their statistical error,
- to test hypotheses with simulations,
- to design simulations to lower the variance of usual simulation estimators, and finally,
- to determine whether the stochastic model chosen is consistent with a set of actual data.
Learning activities
The course will consist of
7 video lectures
6 zoom homework assignments with discussion seminars (Zoom)
1 project - written report and oral presentation session.
The students are expected to solve designated homework problems and send in written solutions to the problems prior to each of the weekly seminars. Further, the students should be prepared to make oral presentations of their solutions in the seminar sessions. The course in concluded with an individual simulation task that the student will present as a written report and in an oral presentation
Detailed plan
| Date | Learning activities | Content | Preparations |
|---|---|---|---|
| Nov 2, 13-15 | Introduction seminar | Course planning | Video Lecture 1 Book ch 1,2 |
| Nov 9, 13-15 | Homework sem 1 | Presentation and discussion of solutions by students |
Video lecture 2, |
| Nov 16, 13-15 | Homework sem 2 | Presentation and discussion of solutions by students | Book ch 5 HW2 |
| Nov 23, 13-15 | Homework sem 3 | Presentation and discussion of solutions by students | Video lecture 3, Book ch 8 HW3 |
| Nov 30, 13-15 | Homework sem 4 | Presentation and discussion of solutions by students | Video lecture 4, Book ch 8 HW4 |
| Dec 7, 13-15 | Homework sem 5 | Presentation and discussion of solutions by students | Video lecture 5, Book ch 9 HW5 |
| Dec 14, 13-15 | Homework sem 6 | Video lecture 6, Book ch 11 HW6 |
|
| Jan TBD | Project presentation | Oral presentation of project report | Written project report |
Preparations before course start
Recommended prerequisites
1.University level course in probability and statistics
2.Basic programming skills, preferably in Matlab
Literature
Sheldon M. Ross, Simulation, Fifth Edition, Academic Press, ISBN-10: 0124158250
Support for students with disabilities
Students at KTH with a permanent disability can get support during studies from Funka:
Examination and completion
Grading scale
P, F
Examination
- EXA1 - Examination, 6.0 credits, Grading scale: P, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
The section below is not retrieved from the course syllabus:
Examination ( EXA1 )
Other requirements for final grade
70% of the homwework problems adequately solved
Passed project report and oral presentation
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
No information inserted
Contacts
Round Facts
Start date
31 Oct 2022
Course offering
- Autumn 2022-51288
Language Of Instruction
English