EG2420 Monte Carlo Simulation Theory and Project 7.5 credits
This course will be discontinued.
Last planned examination: Autumn 2024
Decision to discontinue this course:
The course will be discontinued at the expiration of autumn semester 2024 in accordance with Head of School decision: J-2022-1232.
Decision date: 08/06/2022
The course is given for the last time during the autumn semester of 2022. The final opportunity for the course examination will be given in the autumn semester 2024. Two examination dates will be offered per academic year. Examination will have the same format as earlier. The project assignments can be reported in connection with examinations.
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.
Content and learning outcomes
Course contents
Theory and examples are presented in lectures. These are then applied by the technology students in a number of assignments which are to be solved using appropriate software (for example Matlab). The course will include the following topics:
- general probability theory
- stochastic variables
- random number generation
- simple sampling
- complementary random numbers
- dagger sampling
- control variables
- correlated sampling
- stratified sampling
- weight 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,
• formulate models that suits for Monte Carlo simulation and design appropriate simulation methods for a given model,
• analyse suggested simulation methods and provide constructive critisism.
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)
Equipment
Literature
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
Examination
- PRO1 - Project Work 1, 4.0 credits, grading scale: A, B, C, D, E, FX, F
- TEN1 - Examination, 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.
The final grade is equal to the grade of the project assignment.
Other requirements for final grade
Each examination part should be approved.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
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.