Embracing equifinality with efficiency: limits of acceptability sampling using the DREAM(LOA) algorithm

Jasper A. Vrugt and Keith J. Beven

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: 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; Beven and Freer, 2001; Beven, 2006; Beven et al., 2014). 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.

Keywords: GLUE; Limits of Acceptability; Markov Chain Monte Carlo; Posterior Sampling; DREAM; DREAM(LOA); Sufficiency; Hydrological modelling (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Date: 2018-04-01
New Economics Papers: this item is included in nep-ecm and nep-ore
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Published in Journal of Hydrology, 1, April, 2018, 559, pp. 954-971. ISSN: 0022-1694

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:87291

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