SK3898 Stochastic Methods 5.0 credits

Stokastiska metoder

Please note

The information on this page is based on a course syllabus that is not yet valid.

  • Education cycle

    Third cycle
  • Main field of study

  • Grading scale

At present this course is not scheduled to be offered.

Intended learning outcomes

After completing the course, you should be 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.


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


Enrolled as PhD student.
Ph. D students in computational sciences and e-science.
Basic knowledge in statistics and probability theory and basic knowledge using Matlab/Octave.

Recommended knowledge: Basic courses in programming, matematical statistics and probability theory. 


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.


DAT1: Computer exercises, 1.5 hp credits, grade scale: P/F
PRO1: Project work, 3.5 hp credits, grade scale: P/F

Requirements for final grade

Examination (pass/fail):
* Passing computer exercises
* Project work with oral and written presentation

Offered by

SCI/Applied Physics


Lars Bergqvist (


Lars Bergqvist <>


Course syllabus valid from: Autumn 2018.