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EG2420 Monte Carlo Simulation Theory and Project 7.5 credits

Monte Carlo methods comprises a number of different methods for solving complicated mathematical problems using sample surveys. Applications of Monte Carlo methods can be found in many fields, from opinion polls to simulations of technical systems. The focus of this course is going to be on the latter.

The methods that are taught are general, although many examples in the course are from simulation of electricity markets. However, students do not need to have any previous knowledge of electricity markets to follow these examples.

Choose semester and course offering

Choose semester and course offering to see information from the correct course syllabus and course offering.

Headings with content from the Course syllabus EG2420 (Spring 2019–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

Theory and examples are presented during the lectures and are then applied by the students in a number of home assignments, which are to be solved using appropriate software (for example Matlab). The course will include the following topics:

  • general probability theory
  • random variables
  • random number generation
  • simple sampling
  • complementary random numbers
  • dagger sampling
  • control variates
  • correlated sampling
  • stratified sampling
  • importance sampling

Intended learning outcomes

To pass the course, the students should show that they are able to

• apply method for random number generation, simple sampling and variance reduction techniques,

• formulatem models appropriate for Monte Carlo simulation and design suitable simulation methods,

• analyse suggested simulation methods and provide constructive critisism.

Course disposition

Lessons, seminars, project assignment

Literature and preparations

Specific prerequisites

• SF1625 Calculus in one variable (or equivalent)

• SF1626 Calculus in several variables (or equivalent)

• MJ1520 Statistics and risk assessment or SF1901 Probability theory and statistics (or equivalent)

• English B/English 6 (or equivalent)

Recommended prerequisites

SF1811 or SF1861 Optimization (or equivalent)


No information inserted


M. Amelin, Monte Carlo Methods in Engineering, course compendium

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


  • PRO1 - Project Work 1, 4,0 hp, betygsskala: A, B, C, D, E, FX, F
  • TEN1 - Examination, 3,5 hp, betygsskala: 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 final grade is equal to the grade of the project assignment.

Other requirements for final grade

Each part of the examination must be passed.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted


Profile picture Mikael Amelin

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 web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web EG2420

Offered by

EECS/Electrical Engineering

Main field of study

Electrical Engineering

Education cycle

Second cycle

Add-on studies

No information inserted

Supplementary information

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