Stochastic Methods in Computational Sciences
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.
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.
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: 9783540707127
Complementary literature
J. C. Spall, Introduction to Stochastic Search and Optimization, Wiley 2003, ISBN: 9780471330523
N. G. van Kampen, Stochastic Processes in Physics and Chemistry, Elsevier, ISBN:9780444529657
Course schedule
1 week prestudy exercises 1419 October 2013
1 week lectures and handson 2125 October 2013 at KTH Royal Institute of Technology
1 week project assignment 28 October – 1 November 2013
Teachers
 Lars Bergqvist (lbergqv@kth.se <mailto:lbergqv@kth.se>)

Anders Bergman (anders.bergman@physics.uu.se <mailto:anders.bergman@physics.uu.se>)
Registration
Send an email to lbergqv@kth.se with the following information:
 SeSE, Stochastic methods
 Name
 email (You must use your university email address, not gmail, yahoo, hotmail etc.)
 Affiliation
 Supervisor
 Subject of PhDproject.
Indicate also if you apply for a travel grant.
Deadline for registration: 7 October 2013