Section Mathematical Statistics and Probability Theory
Modern information technology facilitates collection and processing of ever increasing amounts of data. On the one hand, this allows studying increasingly complex models and on the other hand, drawing conclusions from this wealth of data requires novel mathematical techniques.
In light of these developments, statistical analysis requires both a deep understanding of the probability theoretical modeling foundations and new approaches beyond classical methods. Mathematical investigation of those leads to interesting questions at the interface of mathematical statistics, statistical learning, and applied probability theory.
Our work in Heidelberg focuses on
- high dimensional data,
- statistical inverse problems,
- non-parametric statistics,
- statistical learning,
- and the statistics of stochastic processes.
