SK3898 Stochastic Methods 5.0 credits
The information on this page is based on a course syllabus that is not yet valid.
Education cycleThird cycle
Main field of study
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
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
* Passing computer exercises
* Project work with oral and written presentation
Lars Bergqvist (firstname.lastname@example.org)
Lars Bergqvist <email@example.com>
Course syllabus valid from: Autumn 2018.