Michael graduated in the field of Statistics at the Humboldt-Universtät zu Berlin in 2016. This was followed by doctoral studies at the Department of Statistics at the LMU Munich. In 2019 he obtained his PhD with the thesis "New Approaches for Statistical Network Data Analysis". To date, Michael works as a Research Scientist for applied Data Science at Siemens AG, dealing with topics that include anomaly Machine Learning, Explainable AI (XAI) and Statistical Modelling.
In this paper, the authors review the different methods designed to estimate matrixes from their marginals and potentially exogenous information.