While likelihood-based inference and its variants provide a statistically efficient and widely
applicable approach to parametric inference, their application to models involving …

In many applications it is desirable to infer coarse-grained models from observational data.
The observed process often corresponds only to a few selected degrees of freedom of a …

Data-driven stochastic parameterization methods use observational data to support and
improve existing prediction systems. Specifically in atmospheric sciences, uncertainty in …

Modellierung der kleinskaligen Turbulenz spielt in den numerischen
Wettervorhersagesystemen eine entscheidende Rolle, denn die Turbulenz trägt zum …

In this thesis we address the problem of data-driven coarse-graining, ie the process of
inferring simplified models, which describe the evolution of the essential characteristics of a …

This thesis is concerned with the problem of volatility estimation in the context of multiscale
diffusions. In particular, we consider data that exhibit two widely separated time scales …