Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling

By Roberta-Serena Blasone, Jasper A. Vrugt, Henrik Madsen, D. Rosbjerg, Bruce A. Robinson, George Zyvoloski. 2008

351 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/9201c7929f648860d97646897136c31966770375

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In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Indices for assessing the prediction bounds of hydrological models and application by generalised likelihood uncertainty estimation / Indices pour évaluer les bornes de prévision de modèles hydrologiques et mise en œuvre pour une estimation d'incertitude par vraisemblance généralisée (2009, methodology): “…et al., 1999; Aronica et al., 2002; Blazkova & Beven, 2002; Montanari, 2005; Heidari et Lihua Xiong et al. Copyright © 2009 IAHS Press 858 al., 2006; Mantovan & Todini, 2006; McMichael et al., 2006; Beven et al., 2008; Blasone et al., 2008a,b; Xiong & O’Connor, 2008; Stedinger et al., 2008).”
Rating curve estimation under epistemic uncertainty (2015, methodology): “(2008), and used successfully in MCMC sampling schemes for hydrological model calibration (Blasone et al., 2008; McMillan and Clark, 2009).”
Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods (2016, methodology): “…Likelihood Uncertainty Estimation (GLUE) procedure [Beven and Binley, 1992], a Monte Carlo based approach to model training and uncertainty assessment which is employed extensively in hydrological and environmental modeling [Blasone et al., 2008; Bastola et al., 2011; Shafii and Tolson, 2015].”
Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches (2018, background): “Further, its suitability for parallel implementation on distributed computer systems as well as its general strategy in dealing with equifinality in model calibration make it an appealing framework (Blasone et al., 2008; Shen et al., 2012; Mirzaei et al., 2015).”
Evaluation of the subjective factors of the GLUE method and comparison with the formal Bayesian method in uncertainty assessment of hydrological models (2010, neutral): “Based on these two methods or a combination thereof, many methodologies have been developed during the past two decades to better treat uncertainty and they have been applied to various catchments models (Vrugt et al., 2003; Blasone et al., 2008a,b; Vogel et al., 2008; Xiong and O’Connor, 2008).”
Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation (2016, methodology): “Please refer to Schoups and Vrugt (2010a) and Whittle (1953) for a detailed descrip-1284 tion of (14) and (15), Turner and Sederberg (2012) and Sadegh and Vrugt (2014) for the ABC likelihood functions (21-22), and Beven and Freer (2001)1286 for the formulation of the GLUE pseudo-likelihoods (31-34).”
Impact of the numbers of observations and calibration parameters on equifinality, model performance, and output and parameter uncertainty (2015, background): “A GLUE framework has been extensively discussed in terms of subjectivity of selecting a ‘behavioural’ parameter threshold (Beven, 2006; Blasone et al. 2008; Liu et al., 2009; Winsemius et al., 2009) and statistical informality of a generalized likelihood function (Freer et al.”
A comparison of seven methods for the inverse modelling of groundwater flow. Application to the characterisation of well catchments (2009, methodology): “One example is the combination of sequential data assimilation with the Ensemble Kalman Filter and global optimisation [135] or the McMC approach [16,17].”
Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model (2010, methodology): “…over GLUE has recently started to increase ‘‘for not being formally Bayesian, requiring subjective decisions on the likelihood function and cutoff threshold separating behavioral from non-behavioral models, and for not implementing a statistically consistent error model” (Blasone et al., 2008b).”
GLUE: 20 years on (2014, methodology): “…on the basis of genetic algorithm and artificial neural network techniques to map out the areas of high likelihood in the model space, whereas Blasone et al. (2008a,2008b) and McMillan and Clark (2009) have suggested combining GLUE and Markov chain Monte Carlo (MCMC) strategies to increase…”
Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? (2009, methodology): “This paper builds further on our previous work (Blasone et al. 2008) and compares informal GLUE with a formal Bayesian approach using the recently developed differential evolution adaptive metropolis (DREAM) Markov Chain Monte Carlo (MCMC) scheme (Vrugt et al. 2008a, b).”