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FIK3507 Statistical Problems in Simulation 6.0 credits

Course memo Autumn 2024-51431

Version 2 – 09/26/2024, 2:50:09 PM

Course offering

Autumn 2024-51431 (Start date 28 Oct 2024, English)

Language Of Instruction

English

Offered By

EECS/Communication Systems

Course memo Autumn 2024

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

  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.

Learning activities

The course consists of

7 video lectures

6 homework assignments with discussion seminars  (mandatory to attend, Zoom or Kista depending on the distribution of the course participants)

6 exercise sessions (voluntary to attend for a better preparation for homework assignments)

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

Important dates

Date Learning activities Content Preparations
Oct 28, 10-12 Introduction seminar Course planning Video Lecture 1
Book ch 1,2
Nov 6, 10-12 Homework sem 1 Presentation and discussion of solutions by students

Video lecture 2,
Book ch 3,4
HW1

Nov 12, 13-15 Homework sem 2 Presentation and discussion of solutions by students Book ch 5
HW2
Nov 19, 15-17 Homework sem 3 Presentation and discussion of solutions by students Video lecture 3,
Book ch 8
HW3
Nov 28, 08-10 Homework sem 4 Presentation and discussion of solutions by students Video lecture 4,
Book ch 8
HW4
Dec 4, 13-15 Homework sem 5 Presentation and discussion of solutions by students Video lecture 5,
Book ch 9
HW5
Dec 10, 10-12 Homework sem 6   Video lecture 6,
Book ch 11
HW6
Jan TBD Project presentation Oral presentation of project report Written project report

Detailed course schedule can be found at https://intra.kth.se/en/utbildning/schema-och-lokalbokning/sok-schema-1.279132 

Preparations before course start

Literature

Sheldon M. Ross, Simulation, Sixth Edition, Academic Press (pdf download is available through KTH Libraray site)

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

Recommended prerequisites

1.University level knowledge in probability theory and statistics

2.Basic programming skills in Python or Matlab

Round Facts

Start date

28 Oct 2024

Course offering

  • Autumn 2024-51431

Language Of Instruction

English

Offered By

EECS/Communication Systems

Contacts

Course Coordinator

Teachers

Examiner