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• Svenska

# Course memo Autumn 2022

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

• 7 (video) lectures,
• 6  homework discussion seminars,
• one programming assigment and
• one project assignment (simulation task)
• written report
• oral presentation.
• review another groups report

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

31 Oct 2022

### Course offering

• Autumn 2022-50129

English

### Offered By

EECS/Computer Science