Embracing equifinality with efficiency: Limits of Acceptability sampling using the DREAM((LOA)) algorithm

作者：Vrugt Jasper A^*; Beven Keith J

来源：Journal of Hydrology, 2018, 559: 954-971.

DOI：10.1016/j.jhydrol.2018.02.026

• GLUE
• Limits of Acceptability
• Markov Chain Monte Carlo
• Posterior sampling
• DREAM
• DREAM((LOA))
• Sufficiency
• Hydrological modelling

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 (Seven 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.

• 出版日期2018-4