|Position||Master’s student / Research assistant|
|Address||Im Neuenheimer Feld 229
|Group||STACY – Paleoclimate dynamics and variability|
- Bayesian parameter estimation
- Markov Chain Monte Carlo methods
- Energy Balance Models
Maybritt wrote her master’s thesis in the STACY group, where she applied Bayesian methods to estimate parameters of Energy Balance Models. Currently, she continues her work as a research assistant.
More generally, she is interested in applying tools from probability theory and statistics to climate science, for example to analyse extreme events.
M. Schillinger, B. Ellerhoff, K. Rehfeld, R. Scheichl (2021): Bayesian parameter estimation for EBMs: What can we learn about climate variability? DPG Meeting of the Matter and Cosmos Section (SMuK).
- Introduction to Probability and Statistics (2020/21)
- Probability Theory (2021)
- Statistics II (2021/22)
Maybritt loves teaching mathematics to other students and has worked as a tutor multiple times, always with great enthusiasm.
Workshop for school classes: Mathematics of heat records
Climate communication (for examples, see here or here)
Science Academy Baden-Württemberg, for example, teacher of maths course in 2018/19
Maybritt acknowledges a scholarship from the “Studienstiftung des deutschen Volkes”.