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.
Course memo Autumn 2022
Course presentation
Headings denoted with an asterisk ( * ) is retrieved from the course syllabus version Autumn 2022
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.
Learning activities
The course will consist of
- 5 (video) lectures,
- 6 homework discussion seminars,
- one programming assigment and
- one project assignment (simulation task)
- written report
- oral presentation.
- review another groups report
Detailed plan
TDB
Preparations before course start
Literature
Sheldon M Ross, “Simulation” (5th ed), Academic Press, ISBN 9780124158252
Software
MATLAB or Python 3 (including numpy)
Support for students with disabilities
Students at KTH with a permanent disability can get support during studies from Funka:
Examination and completion
Grading scale
A, B, C, D, E, FX, F
Examination
- 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.
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
No information inserted
Contacts
Round Facts
Start date
Missing mandatory information
Course offering
- Autumn 2022-50129
Language Of Instruction
English