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Embracing equifinality with efficiency: Limits of Acceptability sampling using the DREAM(LOA) algorithm
Univ Calif Irvine, Dept Civil & Environm Engn, Irvine, CA USA; Univ Calif Irvine, Dept Earth Syst Sci, Irvine, CA USA; Univ Amsterdam, IBED, Amsterdam, Netherlands.
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL. Univ Lancaster, Lancaster Environm Ctr, Lancaster, England; London Sch Econ, CATS, London, England.ORCID iD: 0000-0001-7465-3934
2018 (English)In: Journal of Hydrology, ISSN 0022-1694, E-ISSN 1879-2707, Vol. 559, p. 954-971Article in journal (Refereed) Published
Abstract [en]

This essay illustrates some recent developments to the DiffeRential Evolution Adaptive Metropolis (DREAM) MATLAB toolbox of Vrugt (2016) to delineate and sample the behavioural solution space of set-theoretic likelihood functions used within the GLUE (Limits of Acceptability) framework (Beven and Binley, 1992, 2014; Beven and Freer, 2001; Beven, 2006). This work builds on the DREAM(ABC) algorithm of Sadegh and Vrugt (2014) and enhances significantly the accuracy and CPU-efficiency of Bayesian inference with GLUE. In particular it is shown how lack of adequate sampling in the model space might lead to unjustified model rejection.

Place, publisher, year, edition, pages
2018. Vol. 559, p. 954-971
Keywords [en]
GLUE, Limits of Acceptability, Markov Chain Monte Carlo, Posterior sampling, DREAM, DREAM((LOA)), Sufficiency, Hydrological modelling
National Category
Oceanography, Hydrology and Water Resources
Identifiers
URN: urn:nbn:se:uu:diva-355685DOI: 10.1016/j.jhydrol.2018.02.026ISI: 000430902000075OAI: oai:DiVA.org:uu-355685DiVA, id: diva2:1230395
Available from: 2018-07-03 Created: 2018-07-03 Last updated: 2018-07-03Bibliographically approved

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Beven, Keith

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