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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.

Pre-requisites

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

Complementary literature

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

Course schedule

1 week pre-study exercises 14-19 October 2013
1 week lectures and hands-on 21-25 October 2013 at KTH Royal Institute of Technology
1 week project assignment 28 October – 1 November 2013

Teachers

Registration

Send an e-mail to lbergqv@kth.se  with the following information:

  • SeSE, Stochastic methods
  • Name
  • e-mail (You must use your university email address, not gmail, yahoo, hotmail etc.)
  • Affiliation
  • Supervisor
  • Subject of PhD-project.

Indicate also if you apply for a travel grant.

Deadline for registration:  7 October 2013