IM3010 Stochastic Methods 5.0 credits

Stokastiska metoder

Contents

Random numbers, optimization methods, Markov processes, Monte Carlo methods and stochastic calculus and differential equations, survey of real world examples of stochastic methods.

Course objectives

When you have finished the course, you are required to show the following skill:

List examples of different stochastic methods and judge when the methods are applicable.

Explain the physical principles and background of Monte Carlo methods and stochastic calculus.

Illustrate and discuss how Monte Carlo methods are constructed.

Learning outcomes

When you have finished the course, you are able to:

List examples of different stochastic methods and judge when the methods are applicable.

Explain the physical principles and background of Monte Carlo methods and stochastic calculus.

Illustrate and discuss how Monte Carlo methods are constructed.

Course schedule

1 week pre-study exercises 

1 week lectures and hands-on compuer exercises

1 week project assignment

Course part of the national e-Science research school SeSE (www.sese.nu)

  • Educational level

    Third cycle
  • Academic level (A-D)

    D
  • Subject area

  • Grade scale

At present this course is not scheduled to be offered.

Intended learning outcomes

When you have finished the course, you are able to:

List examples of different stochastic methods and judge when the methods are applicable.

Explain the physical principles and background of Monte Carlo methods and stochastic calculus.

Illustrate and discuss how Monte Carlo methods are constructed.

Course main content

Random numbers, optimization methods, Markov processes, Monte Carlo methods and stochastic calculus and differential equations, survey of real world examples of stochastic methods.

Disposition

3 weeks format in line with the SeSE course format

1 week pre-study

1 week lectures and hands-on computer exercises

1 week project assignment

Eligibility

Ph. D students in computational sciences and e-science

Basic knowledge in statistics and probability theory and basic knowledge using Matlab/Octave.

Recommended prerequisites

Basic knowledge in statistics and probability theory and basic knowledge using Matlab/Octave.

Literature

C. Gardiner, Stochastic Methods- A handbook for the Natural and Social Sciences , Springer Verlag 2009, ISBN: 978-3-540-70712-7

J. C. Spall, Introduction to Stochastic Search and Optimization, Wiley 2003, ISBN: 978-0-471-33052-3

N. G. van Kampen, Stochastic Processes in Physics and Chemistry, Elsevier, ISBN:978-0-444-52965-7

Required equipment

Laptop with Matlab (or Octave) installed.

Examination

Requirements for final grade

Examination (pass/fail):

Passing computer exercises

Project work with oral and written presentation

Offered by

SCI/Applied Physics

Contact

Lars Bergqvist (lbergqv@kth.se)

Examiner

Lars Bergqvist <lbergqv@kth.se>

Version

Course syllabus valid from: Autumn 2013.