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II2206 Stochastic Simulation 7.5 credits

The course aims at providing the students with the fundamentals of stochastic simulation. The course covers experimental design techniques, implementation of Discrete Event simulations and statistical output analysis and hypothesis testing.

About course offering

For course offering

Autumn 2024 Start 28 Oct 2024 programme students

Target group

Open to all programmes as long as it can be included in your programme.

Part of programme

Master's Programme, Communication Systems, åk 1, ITE, Recommended

Master's Programme, Communication Systems, åk 1, SMK, Recommended

Master's Programme, Communication Systems, åk 1, TRN, Recommended

Master's Programme, ICT Innovation, åk 1, AUSM, Recommended

Master's Programme, ICT Innovation, åk 1, AUSY, Recommended

Master's Programme, Information and Network Engineering, åk 1, Recommended


P2 (7.5 hp)


28 Oct 2024
13 Jan 2025

Pace of study


Form of study

Normal Daytime

Language of instruction


Course location

KTH Kista

Number of places

Places are not limited

Planned modular schedule


For course offering

Autumn 2024 Start 28 Oct 2024 programme students

Application code



For course offering

Autumn 2024 Start 28 Oct 2024 programme students


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Course coordinator

No information inserted


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Headings with content from the Course syllabus II2206 (Autumn 2022–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The course contains the following parts:

  • Introduction to simulation – resource efficiency design of complex systems
  • Stochastic Modelling
  • Random number generation
  • Simulation of discrete event
  • Output analysis parameter estimation, error estimation, time series analysis, ergodicity and correlation
  • Experimental design and methods for variance reduction
  • Hypothesis test and model validation

Intended learning outcomes

After passing the course, the student shall be able to

  • generate stochastic variables (random number) with arbitrary distribution
  • design simulations with discrete events
  • estimate parameters from the simulation results and the statistical error of the estimates
  • test hypotheses by means of simulations
  • evaluate the chosen stochastic model with regard to consistency with real data
  • evaluate the resource efficiency that simulation tools can give in relation to traditional experimental methods.

For higher grades, the student should also be able to

  • generate vectors of random number with given correlation properties
  • estimate parameters in correlated time series
  • evaluate different simulation methods with regard to resource efficiency and design efficient simulation strategies through different methods for variance reduction.

Literature and preparations

Specific prerequisites

Knowledge and skills in programming, 5 credits, equivalent to completed course DD1337/DD1310-DD1318/DD1321/DD1331/DD100N/ID1018.

Knowledge in mathematical statistics, 6 higher education credits, equivalent completed course SF1910-SF1926/IX1501.

Recommended prerequisites

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Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F


  • HEM1 - Home Assignments, 4.0 credits, grading scale: A, B, C, D, E, FX, F
  • PRO1 - Project, 3.5 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.

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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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

Main field of study

Computer Science and Engineering, Electrical Engineering

Education cycle

Second cycle

Add-on studies

No information inserted

Supplementary information

In this course, the EECS code of honor applies, see: