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Bayesian multi-model frameworks - Properly Addressing Conceptual Uncertainty in Applied Modelling

. Universität Tübingen, Tübingen, Germany, Doctoral dissertation, (2019)

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Using model complexity to select the optimum architecture for artificial neural networks, , , , and . 20th EGU General Assembly, EGU2018, page 17908. Smithsonian Astrophysical Observatory, (2018)Diagnosing similarities in probabilistic multi-model ensembles : an application to soil–plant-growth-modeling, , , , , , and . Modeling earth systems and environment, 8 (4): 5143-5175 (2022)Bayesian multi-model frameworks - Properly Addressing Conceptual Uncertainty in Applied Modelling. Universität Tübingen, Tübingen, Germany, Doctoral dissertation, (2019)Bayesian model evidence as a model evaluation metric, , and . 19th EGU General Assembly, EGU2017, page 13390. Smithsonian Astrophysical Observatory, (2017)The hydrologist's guide to Bayesian model selection, averaging and combination, , and . Journal of Hydrology, (2019)Strategies for Simplifying Reactive Transport Models : A Bayesian Model Comparison, , , , and . Water resources research, 56 (11): e2020WR028100 (2020)Lumped geohydrological modelling for long-term predictions of groundwater storage and depletion, , , and . Journal of hydrology, 606 (March): 127347 (2022)How model selection and averaging strategies help us improve hydrological models, , and . 20th EGU General Assembly, EGU2018, page 12797. Smithsonian Astrophysical Observatory, (2018)Bayesian Multi-Model Frameworks : Properly Addressing Conceptual Uncertainty in Applied Modelling. Universität Tübingen, Tübingen, Dissertation, (2019)A Primer for Model Selection: The Decisive Role of Model Complexity, , and . Water resources research, 54 (3): 1688-1715 (2018)