Dieses Bild zeigt Marvin Höge

Marvin Höge

Herr Dr.

Postdoktorand
Institut für Wasser- und Umweltsystemmodellierung (LS3/SimTech)

Kontakt

+49 711 685 60131
Visitenkarte (VCF)

Pfaffenwaldring 5a
D-70569 Stuttgart
Raum: 2.29

  1. 2020

    1. Höge M, Guthke A, Nowak W. Bayesian Model Weighting: The Many Faces of Model Averaging. Water [Internet]. 2020;12(2):309. Verfügbar unter: https://www.mdpi.com/2073-4441/12/2/309
    2. Sinsbeck M, Höge M, Nowak W. Exploratory-phase-free estimation of GP hyperparameters in sequential design methods - at the example of Bayesian inverse problems. Frontiers in Artificial Intelligence, section AI in Food, Agriculture and Water. 2020;3(52):1–16.
    3. Schäfer Rodrigues Silva A, Guthke A, Höge M, Cirpka OA, Nowak W. Strategies for simplifying reactive transport models - a Bayesian model comparison. Water Resources Research. 2020;56:e2020WR028100.

  2. 2019

    1. Höge M. Bayesian multi-model frameworks - Properly Addressing Conceptual Uncertainty in Applied Modelling [Internet] [Doctoral dissertation]. Universität Tübingen, Tübingen, Germany; 2019. Verfügbar unter: https://publikationen.uni-tuebingen.de/xmlui/handle/10900/87769
    2. Höge M, Guthke A, Nowak W. The Hydrologist’s Guide to Bayesian Model Selection, Averaging and Combination. Journal of Hydrology [Internet]. Mai 2019;572:96–107. Verfügbar unter: http://www.sciencedirect.com/science/article/pii/S0022169419301532

  3. 2018

    1. Höge M, Wöhling T, Nowak W. A Primer for Model Selection: The Decisive Role of  Model Complexity. Water Resources Research [Internet]. 2018; Verfügbar unter: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR021902
    2. Höge M, Wöhling T, Nowak W. The Decisive Role of Model Complexity in Model Selection. In: General Assembly 2018, Geophysical Research Abstracts  20: EGU2018-6499, 2018. Vienna, Austria: European Geosciences Union (EGU); 2018. (General Assembly 2018, Geophysical Research Abstracts  20: EGU2018-6499, 2018).
    3. Guthke A, Höge M, Nowak W. How model selection and averaging strategies help us improve hydrological models. In: General Assembly 2018, Geophysical Research Abstracts 20: EGU2018-12797, 2018. Vienna, Austria: European Geosciences Union (EGU); 2018. (General Assembly 2018, Geophysical Research Abstracts 20: EGU2018-12797, 2018).
    4. Khan U, Snieder E, R.Shakir, Höge M, Nowak W. Using model complexity to select the optimum architecture  for artificial neural networks. In: General Assembly 2018, Geophysical Research Abstracts 20: EGU2018-17908. Vienna, Austria: European Geosciences Union (EGU); 2018. (General Assembly 2018, Geophysical Research Abstracts 20: EGU2018-17908).
    5. Höge M, Wöhling T, Nowak W. Model Selection: Play-It-Safe vs. No-Risk-No-Fun. In: Integrated Hydrosystem Modelling 2018 Conference: How Complex Should Integrated Models Be? Tübingen, Germany: RTG 1829, DFG; 2018. (Integrated Hydrosystem Modelling 2018 Conference: How Complex Should Integrated Models Be?).

  4. 2017

    1. Guthke A, Höge M, Nowak W. Bayesian model evidence as a model evaluation metric. In: General Assembly 2017, Geophysical Research Abstracts 19: EGU2017-13390-1, 2017. Vienna, Austria: European Geosciences Union (EGU); 2017. (General Assembly 2017, Geophysical Research Abstracts 19: EGU2017-13390-1, 2017).
    2. Höge M, Illman W, Nowak W. Bayesian Model Selection under Time Constraints. In: Fall Meeting 2017, Abstract: H23C-1661. New Orleans, LA, USA: American Geophysical Union (AGU); 2017. (Fall Meeting 2017, Abstract: H23C-1661).

  5. 2016

    1. Höge M, Wöhling T, Nowak W. On the Way to Appropriate Model Complexity. In: Fall Meeting 2016, Abstract: NG13A-1683. San Francisco, CA, USA: American Geophysical Union (AGU); 2016. (Fall Meeting 2016, Abstract: NG13A-1683).

Seit 10/2019 Postdoktorand, Institut für Wasser- und Umweltsystemmodellierung, Universität Stuttgart
04/2019 Dr. rer. nat, Universität Tübingen
Seit 10/2015 Wissenschaftlicher Mitarbeiter, Institut für Wasser- und Umweltsystemmodellierung, Universitäten Stuttgart und Tübingen
09/2015 M.Sc. Applied and Environmental Geoscience, Universität Tübingen
09/2013 B.Sc. Umweltnaturwissenschaften, Universität Tübingen

Bayes'sche Modellwahl, -mittelung und -kombination

Stochastische Modellierung von integrierten Hydrosystemen

Wissenschaftliches maschinelles Lernen

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