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A specialization in Computational Stochastics is based on a number of core courses, primarily from Analysis and Stochastics, which are outlined below. Prospective Bachelor students may also want to look at an elementary introduction to Computational Stochastics.


For Bachelor students the recommendations for a specialization in Computational Stochastics and in Functional Analysis and Stochastic Analysis are nearly identical. In the 2nd and 3rd year students take the courses

  • Stochastische Methoden
  • Maß- und Integrationstheorie
  • Einführung: Funktionalanalysis
  • Monte Carlo Algorithms
  • Probability Theory
  • Functional Analysis

The courses

  • Einführung in die Numerik
  • Einführung: Gewöhnliche Differentialgleichungen

are recommended, too, for the 2nd year Bachelor studies. A schedule for Bachelor students can be found here.

In this way students are well prepared for a Bachelor thesis in Computational Stochastics with a focus on theory or on an application and for a subsequent Master program.


Master students with a specialization in Computational Stochastics start with the course on

  • Stochastic Differential Equations

which is followed by special courses like Introduction to Stochastic Partial Differential Equations, Numerics of Stochastic Processes or High-dimensional Integration.

Master students are encouraged to also take courses from nearby mathematical areas like

  • Stochastic Analysis
  • Image Analysis
  • Approximation Theory
  • Financial Mathematics
  • Theory and Numerics of PDEs

Typically, the topics of Master theses are closely related to ongoing research projects of the Computational Stochastics group.