Information on the lecture Measure Theory (WS 2024/2025)

Lecturers: Prof. Dr. Peter Pfaffelhuber

Assistant: Samuel Adeosun

Date: Lecture (2 hours): asynchronous videos

Exercise: 2 hours, date to be determined

ETCS: 6 points

Language: English

Contents

Measure Theory is the foundation of advanced probability theory. In this course, we build on knowledge in analysis
and provide all necessary results for later classes in statistics, probabilistic machine learning and stochastic processes.
It contains set systems, constructions of measures using outer measures, the integral, and product measures.

News

Literatur

• H. Bauer. Measure and Integration Theory. deGruyter, 2001.
• V. Bogatchev. Measure Theory. Springer, 2007.
• O. Kallenberg. Foundations of Modern Probability Theory. Springer, 2021.

Necessary prior knowledge

Basic courses in analysis, and an understanding of mathematical proofs.
 

Remark

Usable in the following modules:

Elective in Data (MScData24)

Consulting hours

Lecturer consultation hours: by appointment
Assistent consultation hours: by appointment