Articles | Volume 18, issue 5
https://doi.org/10.5194/gmd-18-1709-2025
https://doi.org/10.5194/gmd-18-1709-2025
Methods for assessment of models
 | 
12 Mar 2025
Methods for assessment of models |  | 12 Mar 2025

Selecting a conceptual hydrological model using Bayes' factors computed with replica-exchange Hamiltonian Monte Carlo and thermodynamic integration

Damian N. Mingo, Remko Nijzink, Christophe Ley, and Jack S. Hale

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Cited articles

Allanach, B. C. and Lester, C. G.: Sampling Using a “Bank” of Clues, Comp. Phys. Commun., 179, 256–266, https://doi.org/10.1016/j.cpc.2008.02.020, 2008. a
Ashton, G., Bernstein, N., Buchner, J., Chen, X., Csányi, G., Fowlie, A., Feroz, F., Griffiths, M., Handley, W., Habeck, M., Higson, E., Hobson, M., Lasenby, A., Parkinson, D., Pártay, L. B., Pitkin, M., Schneider, D., Speagle, J. S., South, L., Veitch, J., Wacker, P., Wales, D. J., and Yallup, D.: Nested Sampling for Physical Scientists, Nature Reviews Methods Primers, 2, 39, https://doi.org/10.1038/s43586-022-00121-x, 2022. a
Bai, Z., Rao, C. R., and Wu, Y.: Model selection with data-oriented penalty, J. Stat. Plan. Infer., 77, 103–117, https://doi.org/10.1016/S0378-3758(98)00168-2, 1999. a
Berger, J. O. and Pericchi, L. R.: The Intrinsic Bayes Factor for Model Selection and Prediction, J. Am. Stat. Assoc., 91, 109–122, https://doi.org/10.1080/01621459.1996.10476668, 1996. a
Berger, J. O., Pericchi, L. R., Ghosh, J. K., Samanta, T., De Santis, F., Berger, J. O., and Pericchi, L. R.: Objective Bayesian Methods for Model Selection: Introduction and Comparison, Lecture Notes-Monograph Series, 38, 135–207, 2001. a
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Short summary
Hydrologists are often faced with selecting amongst a set of competing models with different numbers of parameters and ability to fit available data. Bayes’ factor is a tool that can be used to compare models; however, it is very difficult to compute Bayes' factor numerically. In our paper, we explore and develop highly efficient algorithms for computing Bayes’ factor of hydrological systems, which will introduce this useful tool for selecting models into everyday hydrological practice.
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