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Embracing equifinality with efficiency: Limits of Acceptability sampling using the DREAM(LOA) algorithm
- Vrugt, Jasper A., Beven, Keith J.
- Journal of hydrology 2018 v.559 pp. 954-971
- Bayesian theory, algorithms, hydrology, models, sampling
- 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.