The lecture has already taken place.
|Instructor:||Prof. Dr. Nina Gantert|
|Prerequisites:||Measure and integration theory |
Lecture notes of Prof. Lasser's lecture are available here. (in German)
Based on measure and integration theory, in this course the fundamental concepts of probability theory are presented. Of central importance are the different types of convergence, the concept of stochastic independence, and conditional expectations. In addition, the course will deal with the Radon-Nikodym theorem, product spaces and the construction of stochastic processes. For sequences of independent random variables, laws of large numbers and the central limit theorem will be proved. As an important generalization of partial sums of independent random variables, martingales will be introduced and on this basis we will investigate stopping times and prove the martingale convergence theorem.
Rick Durrett: Probability: Theory and Examples,
Allan Gut: Probability: A Graduate Course,
Achim Klenke: Wahrscheinlichkeitstheorie,
Lecture notes are available here.
The exams have already taken place.
|Dates of classes:||The exercise classes have already taken place.|
|Organization of classes:||Christian Bartsch, Mikael Falconnet|
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If you have any questions concerning the exercise classes, please contact Christian Bartsch.