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FIK3507 Statistical Problems in Simulation 6.0 credits

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.

Course syllabus FIK3507 (Spring 2019–)
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

  1. generate random variables of arbitrary distributions,
  2. make parameter estimates based on simulation results and assess their statistical error,
  3. to test hypotheses with simulations,
  4. to design simulations to lower the variance of usual simulation estimators, and finally,
  5. 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.

Other requirements for final grade

70% of the homwework problems adequately solved 

Passed project report and oral presentation

Examiner

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

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.

Offered by

Education cycle

Third cycle

Postgraduate course

Postgraduate courses at EECS/Communication Systems