Foto: Matthias Friel
The course is organised as "inverted class room".
The students are weekly invited to
1- study first alone at home some precise topics (indicated on the Moodle page)
2- meet on Zoom with the Professor for discussion and comments on that topic
3- meet on Zoom with a PhD student as Tutor
4- solve an Exercise sheet, and participate to a meeting where the solutions are discussed.
Precise data (zoom address, timetable etc) are given on the corresponding Moodle web-page
Durrett, R. : Probability: theory and examples.
Cambridge Series in Statistical and Probabilistic Mathematics 2010
Every one is invited to also participate at the moodle course "Introductory to Master in Mathematics WS 2020".
The participant is assumed to have a reasonable grasp of probability theory, analysis, functional analysis and measure theory.
Written or oral exam
The purpose of this course is to treat in details selected fundamentals of modern probability theory. The focus is in particular on limit theorems including the strong law of large numbers and Lindeberg central limit theorem, and on discrete-time processes like martingales, as well as basic results on Brownian motion. Various examples will be considered.
The participant is assumed to have a reasonable grasp of probability, analysis, functional analysis and measure theory.
This lecture is appropriate for Master students in Mathematics and for advanced Bachelor students in Mathematics. It also adresses to students of Data Science, informatics and physics. It is part of both profiles "Mathematical modelling and data analysis" and "Structures of Mathematics with physical background" in the course of studies Master of Science Mathematics.
This lecture is appropriate for Master students in Mathematics and for advanced Bachelor students in Mathematics.
It also adresses to students of Data Science, Informatics and Physics.
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