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II2206 Stochastic Simulation 7,5 hp

Course memo Autumn 2022-50129

Version 1 – 08/28/2022, 7:02:34 PM

Course offering

Autumn 2022-1 (Start date 31/10/2022, English)

Language Of Instruction

English

Offered By

EECS/Computer Science

Course memo Autumn 2022

Course presentation

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.

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)

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

Round Facts

Start date

Missing mandatory information

Course offering

  • Autumn 2022-50129

Language Of Instruction

English

Offered By

EECS/Computer Science

Contacts

Course Coordinator

Teachers

Examiner