Hannah Blocher

I started as a PhD student at the Institute of Statistics in February 2021. I obtained a Bachelor's Degree (B.A.) in Mathematics form the LMU Munich and a Master's Degree (M.Sc) ins Statistics form the LMU Munich

My current research focus is on embedding data depth functions into the theory of formal concept analysis. With this we define statistical models and tests.

Research Interests:

• Data Depth Functions
• Formal Concept Analysis
• Partial Orders
• (Spatial Statistics, Decision Theory)

Article

• Julian Rodemann and Hannah Blocher (2024): Partial Rankings of Optimizers. https://arxiv.org/abs/2402.16565  [last accessed 29.02.2024]
• Hannah Blocher, Georg Schollmeyer, Malte Nalenz and Christoph Jansen (2024): Comparing Machine Learning Algorithms by Union-Free Generic Depth. URL https://arxiv.org/abs/2312.12839 [last accessed 29.02.2024]
• Hannah Blocher, Georg Schollmeyer (2023): Data depth functions for non-standard data by use of formal concept analysis. URL https://arxiv.org/abs/2402.16560 [last accessed 29.02.2024]
• Christoph Jansen, Georg Schollmeyer, Hannah Blocher, Julian Rodemann and Thomas Augustin (2023): Robust statistical comparison of random variables with locally varying scale of measurement. Uncertainty in Artificial Intelligence (UAI 2023). PMLR. [link, preprint, poster]
• Hannah Blocher, Georg Schollmeyer, Christoph Jansen and Malte Nalenz (2023): Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms. In: Proceedings of the Thirteenth International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA '23). Proceedings of Machine Learning Research, vol. 215. PMLR. [link, preprint]
• Georg Schollmeyer and Hannah Blocher (2023) A note on the connectedness property of union-free generic sets of partial orders. URL https://arxiv.org/abs/2304.10549 [last accessed 29.02.2024]
• Hannah Blocher, Georg Schollmeyer and Christoph Jansen (2022): Statistical models for partial orders based on data depth and formal concept analysis. In: Ciucci, D.; Couso, I.; Medina, J.; Slezak, D.; Petturiti, D.; Bouchon-Meunier, B.; Yager, R.R. (eds): Information Processing and Management of Uncertainty in Knowledge-Based Systems. Communications in Computer and Information Science, vol 1602, Springer. [link, preprint: see "Downloads" below]

• Christoph Jansen, Hannah Blocher, Thomas Augustin and Georg Schollmeyer (2022): Information efficient learning of complexly structured preferences: Elicitation procedures and their application to decision making under uncertainty. International Journal of Approximate Reasoning, 144 : 69 - 91. [link, preprint]

Presentations

• Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms (at the Research Methods and Statistics Group, Institute of Psychology, University of Hamburg) [slides: see "Downloads" below]
• Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms (at the Knowledge & Data Engineering Group (KDE), EECS, University of Kassel) [slides: see "Downloads" below]
• Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms (at ISIPTA 2023) [slides: see "Downloads" below]
• Statistical Models for Partial Orders based on Data Depth and Formal Concept Analysis (at IPMU 2022) [slides: see "Downloads" below]
• Statistical Models for Partial Orders based on Data Depth and Formal Concept Analysis (at DAGStat 2022) [slides: see "Downloads" below]
• A Statistical Depth Function for Non-Standard Data based on Formal Concept Analysis (at ECDA 2021) [slides: see "Downloads" below]

Teaching

• WS 2023/24: Seminar on statistical analysis of areal data, spatial point processes and related topics (Bachelor and Master)
• SS 2022: Methoden der lineare Algebra in der Statistik (Bachelor)
• SS 2022: Einführung in die Wahrscheinlichkeitsrechnung und in die induktive Statistik (Bachelor)
• WS 2021/22: Statistical Inference (Master)
• WS 2021/22: Measure and Probability Theory (Master)
• WS 2021/22: CoVorMa - Coronafester Vorkurs für Mathefrustrierte (offen für alle LMU Studierende)
• SS 2021: Einführung in die Wahrscheinlichkeitsrechnung und in die induktive Statistik (Bachelor)
• SS 2021: CoVorMa - Coronafester Vorkurs für Mathefrustrierte (offen für alle LMU Studierende)