Institute of Computational Engineering, Department of Engineering, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue de la Fonte, 4364 Esch-sur-Alzette, Luxembourg
Catchment and Eco-hydrology (CAT), Environmental Sensing and Modelling (ENVISION), Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg
Department of Mathematics, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue de la Fonte, 4364 Esch-sur-Alzette, Luxembourg
Institute of Computational Engineering, Department of Engineering, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue de la Fonte, 4364 Esch-sur-Alzette, Luxembourg
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Total article views: 2,110 (including HTML, PDF, and XML)
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HTML: 1,630
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Viewed (geographical distribution)
Total article views: 4,486 (including HTML, PDF, and XML)
Thereof 4,460 with geography defined
and 26 with unknown origin.
Total article views: 2,110 (including HTML, PDF, and XML)
Thereof 2,089 with geography defined
and 21 with unknown origin.
Total article views: 2,376 (including HTML, PDF, and XML)
Thereof 2,371 with geography defined
and 5 with unknown origin.
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.
Hydrologists are often faced with selecting amongst a set of competing models with different...
