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, test hypotheses with simulations, design simulations to lower the variance of usual simulation estimators, and finally determine whether the stochastic model chosen is consistent with a set of actual data.
Information per course offering
Course offerings are missing for current or upcoming semesters.
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Headings with content from the Course syllabus FIK3507 (Spring 2019–) are denoted with an asterisk ( )
Content and learning outcomes
Course disposition
The course will consist of 6 seminars and one project presentation session. The course will use the "reverse classroom" paradigm, i.e., the seminars are totally devoted to student driven activity (e.g., homework presentations and advising driven by student questions). There will be no conventional lecturing during the seminars, instead the lectures will be presented in videos that the students are expected to have worked through, before the seminar.
The course is concluded with an individual simulation task that the student will present as a written report and in an oral presentation.
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.
Literature and preparations
Specific prerequisites
No information inserted
Recommended prerequisites
1.University level course in probability and statistics
2.Basic programming skills, preferably in Matlab
Literature
You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.
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.
If the course is discontinued, students may request to be examined during the following two academic years.
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
Course room in Canvas
Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.
