Publications

Publications

  1. 2022

    1. H. Hsueh, A. Guthke, T. Wöhling, and W. Nowak, “Diagnosis of model-structural errors with a sliding time-window Bayesian analysis,” Water Resources Research, vol. 58, p. e2021WR030590, 2022, doi: doi:10.1029/2021WR030590.

  2. 2021

    1. S. Reuschen, A. Guthke, and W. Nowak, “The Four Ways to Consider Measurement Noise in Bayesian Model Selection - And Which One to Choose,” Water Resources Research, vol. 57, p. e2021WR030391, 2021, doi: 10.1029/2021WR030391.
    2. I. Banerjee, A. Guthke, C. J. C. Van de Ven, K. G. Mumford, and W. Nowak, “Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data,” Water Resources Research, vol. 57, no. 7, Art. no. 7, 2021, doi: 10.1029/2021WR029986.

  3. 2020

    1. A. Schäfer Rodrigues Silva, A. Guthke, M. Höge, O. A. Cirpka, and W. Nowak, “Strategies for simplifying reactive transport models - a Bayesian model comparison,” Water Resources Research, vol. 56, p. e2020WR028100, 2020, doi: 10.1029/2020WR028100.
    2. H.-F. Hsueh, A. Guthke, T. Wöhling, and W. Nowak, “Diagnosing Model-structural Errors with a Sliding Time-window Bayesian Analysis,” online, Dec. 2020.
    3. M. Höge, A. Guthke, and W. Nowak, “Bayesian Model Weighting: The Many Faces of Model Averaging,” Water, vol. 12, no. 2, Art. no. 2, 2020, doi: 10.3390/w12020309.
    4. A. Guthke, “The Unified Risk Equation - An Attempt to Unify Risk Assessment across Disciplines,” Munich, Germany, Apr. 2020.
    5. I. Banerjee, A. Guthke, C. J. C. V. D. Ven, K. Mumford, and W. Nowak, “Overcoming the Model-to-Experimental Data Fit Problem in Porous Media: a New Quantitative Method to Evaluate and Compare Models,” online, Dec. 2020.
    6. C. Jackisch, A. Schibalski, B. Schröder, W. Nowak, and A. Guthke, “Providing relevant uncertainty information to decision makers: Subjective post-processing of rigorous Bayesian uncertainty assessment of model projections,” online, Dec. 2020.
    7. A. Guthke et al., “A unified framework for quantitative interdisciplinary flood risk assessment,” online, Dec. 2020.

  4. 2019

    1. D. F. Motavita, R. Chow, A. Guthke, and W. Nowak, “The Comprehensive Differential Split-Sample Test: A stress-test for hydrological model robustness under climate variability,” Journal of Hydrology, vol. 573, pp. 501–515, 2019, doi: 10.1016/j.jhydrol.2019.03.054.
    2. M. Höge, A. Guthke, and W. Nowak, “The Hydrologist’s Guide to Bayesian Model Selection, Averaging and Combination,” Journal of Hydrology, vol. 572, pp. 96–107, May 2019, doi: https://doi.org/10.1016/j.jhydrol.2019.01.072.
    3. A. Guthke, “Justifiability is key - Bayesian analysis of system and model complexity,” Esch-sur-Alzette, Luxembourg, Oct. 2019.

  5. 2018

    1. A. Guthke, M. Höge, and W. Nowak, “How model selection and averaging strategies help us improve hydrological models,” Vienna, Austria, Apr. 2018.
    2. A. Guthke and W. Nowak, “Entropy-based experimental design for optimal model discrimination in the Geosciences,” Santander, Spain, May 2018.
    3. Anneli. Guthke, “Model selection on solid ground: rigorous comparison of nine ways to evaluate Bayesian evidence,” Adelaide, Australia, Sep. 2018.
    4. S. Oladyshkin, A. Guthke, F. Mohamadi, R. Kopmann, and W. Nowak, “Model selection under computational time constraints: application to river engineering,” Saint-Malo, France, Jun. 2018.
    5. A. Schäfer-Rodrigues-Silva, A. Guthke, and W. Nowak, “The importance of model similarity in multi-model problems,” Stuttgart, Germany, Feb. 2018.
    6. A. Guthke, S. Oladyshkin, F. Mohammadi, R. Kopmann, and W. Nowak, “Bayesian model selection under computational time constraints: application to river modeling,” Washington, D.C., USA, Dec. 2018.
    7. P. Darscheid, A. Guthke, and U. Ehret, “A Maximum-Entropy Method to Estimate Discrete Distributions  from Samples Ensuring Nonzero Probabilities,” Entropy, vol. 20, no. 8, Art. no. 8, 2018, doi: 10.3390/e20080601.
    8. A. Schäfer-Rodrigues-Silva, T. Seitz, Anneli. Guthke, and W. Nowak, “Quantifying and visualizing similarity in multi-model ensembles,” Cargese, France, Jun. 2018.
    9. A. Guthke, “A Bayesian take on model choice uncertainty: Statistical tools for model evaluation, selection and combination,” Karlsruhe, Germany, Jul. 2018.
    10. A. Schäfer-Rodrigues-Silva, Anneli. Guthke, and W. Nowak, “Quantifying similarity in multi-model ensembles,” Tübingen, Germany, Apr. 2018.
    11. F. Mohammadi, R. Kopmann, A. Guthke, S. Oladyshkin, and W. Nowak, “Bayesian selection of hydro-morphodynamic models under computational  time constraints.,” Advances in Water Resources, vol. 117, pp. 53–64, 2018, doi: 10.1016/j.advwatres.2018.05.007.
    12. A. Schäfer-Rodrigues-Silva, T. Seitz, Anneli. Guthke, and W. Nowak, “Working with multi-model ensembles - what makes models differ and how can we visualize ensembles?,” Tübingen, Germany, Jun. 2018.

  6. 2017

    1. A. Guthke, M. Höge, and W. Nowak, “Bayesian model evidence as a model evaluation metric,” Vienna, Austria, Apr. 2017.
    2. A. Guthke, “Defensible Model Complexity: A Call for Data-Based and Goal-Oriented Model Choice,” Groundwater, vol. 55, no. 5, Art. no. 5, 2017, doi: 10.1111/gwat.12554.

  7. 2016

    1. O. Lötgering-Lin, A. Schöniger, W. Nowak, and J. Gross, “Bayesian Model Selection helps to Choose Objectively between Thermodynamic Models: A Demonstration of Selecting a Viscosity-Model based on Entropy Scaling,” Industrial & Engineering Chemistry Research, vol. 55, no. 38, Art. no. 38, 2016, doi: 10.1021/acs.iecr.6b02671.
    2. W. Nowak and A. Guthke, “Entropy-based experimental design for optimal model discrimination in the geosciences,” Entropy, vol. 18, no. 11, Art. no. 11, 2016, doi: 10.3390/e18110409.
    3. A. Schöniger, W. A. Illman, T. Wöhling, and W. Nowak, “Which level of model complexity is justified by your data? A Bayesian answer,” Vienna, Austria, Apr. 2016.

  8. 2015

    1. A. Schöniger, T. Wöhling, and W. Nowak, “A statistical concept to assess the uncertainty in Bayesian model weights and its impact on model ranking,” Water Resources Research, vol. 51, no. 9, Art. no. 9, 2015, doi: 10.1002/2015WR016918.
    2. W. Nowak, T. Wöhling, and A. Schöniger, “Lessons learned from a past series of Bayesian model averaging studies for soil/plant models,” Vienna, Austria, Apr. 2015.
    3. T. Wöhling, A. Schöniger, S. Gayler, and W. Nowak, “Bayesian model averaging to explore the worth of data for soil-plant model selection and prediction,” Water Resources Research, vol. 51, no. 4, Art. no. 4, 2015, doi: 10.1002/2014WR016292.
    4. A. Schöniger, T. Wöhling, L. Samaniego, and W. Nowak, “On the various (good and bad) ways to evaluate Bayesian model weights,” Vienna, Austria, Apr. 2015.
    5. A. Schöniger, W. Illman, T. Wöhling, and W. Nowak, “Finding the Right Balance Between Groundwater Model Complexity and Experimental Effort via Bayesian Model Selection,” Journal of Hydrology, vol. 531, no. 1, Art. no. 1, 2015, doi: 10.1016/j.jhydrol.2015.07.047.
    6. O. Lötgering-Lin, M. Hopp, J. Gross, A. Schöniger, and W. Nowak, “Prediction of pure component and mixture viscosities using PCP-SAFT and entropy scaling,” Houston, TX, USA, May 2015.
    7. A. Schöniger, “Multi-model approaches to quantify conceptual uncertainty in environmental modelling,” Tübingen, Germany, Apr. 2015.

  9. 2014

    1. A. Schöniger, T. Wöhling, and W. Nowak, “How to address measurement noise in Bayesian model averaging,” San Francisco, CA, USA, Dec. 2014.
    2. A. Schöniger, T. Wöhling, and W. Nowak, “How reliable is Bayesian model averaging under noisy data? Statistical assessment and implications for robust model selection,” Vienna, Austria, 2014.
    3. Anneli. Schöniger, T. Wöhling, L. Samaniego, and W. Nowak, “Model selection on solid ground: rigorous comparison of nine ways to evaluate Bayesian evidence,” Water Resources Research, vol. 50, no. 12, Art. no. 12, 2014, doi: 10.1002/2014WR016062.

  10. 2013

    1. A. Schöniger and W. N. T. Wöhling, “Do Bayesian model weights tell the whole story? New analysis and optimal design tools for maximum-confidence model selection,” San Francisco, CA, USA, Dec. 2013.

  11. 2012

    1. A. Schöniger, W. Nowak, and H. J. H. Franssen, “Parameter estimation by ensemble Kalman filters with transformed data: Approach and application to hydraulic tomography,” Water Resources Research, vol. 48, no. W04502, Art. no. W04502, 2012, doi: 10.1029/2011WR010462 (was the top cited 2012 WRR article in 2013).
To the top of the page