Monte Carlo Methods

Prof. Dr. Verena Wolf
Michael Backenköhler, MSc


Please use the seminar assignment system to register.


The core idea of Monte Carlo (MC) methods is to perform computer simulations of a real-world system based on pseudo-random numbers. MC is applied in a large variety of research areas: physical sciences, computer sciences, engineering, statistics, finance, etc. Moreover, ideas that have originally been developed in the context of MC are meanwhile also in use for sampling problems in the area of machine learning. Although, being a simple and very direct approach to analyze a system, MC methods come with a lot of challenges, in particular, when sampling rare events or when systems have multiple time scales. In this seminar, we take a computer science perspective on Monte Carlo and cover different MC algorithms to tackle these challenges.

date topic
7. 12. Variance Reduction
14. 12. Random Process Generation
4. 1. Probabilistic Programming
11. 1. Markov Chain Monte Carlo
18. 1. Stochastic Gradient Descent
25. 1. Rare Event Simulation and Cross-Entropy Method
1. 2. Evolutionary Algorithms


  • All seminar meetings will take place online (no physical meetings).
  • A Kick-off meeting will take place on the November, 6 at 11:00am.
  • The seminar takes place Mondays at 9:00am (sharp). A list of dates and topics can be found above and in the CMS.
  • The seminar language is English.
  • The seminar earns you 7 ECTS.


The final grade consists of: your presentation (50%), reports (40%), and participation during the seminar discussions (10%).


You should have successfully passed MfI3, the Statistics Lab, or a comparable course covering probability theory/statistics.