Toward diagnostic model calibration and evaluation: Approximate Bayesian computation

By Jasper A. Vrugt, Mojtaba Sadegh. 2013

184 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5c563e5a45eb6c81a30f9af6b29f2a1d4bd63d16

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Citations in the corpus

Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation (2013, methodology): “In the limit of going to zero, the behavioral solution space should converge to the actual posterior distribution, pending the assumption that the chosen summary statistic(s) is (are) near sufficient (Pritchard et al., 1999; Vrugt and Sadegh, 2013).”
Modeling Soil Processes: Review, Key Challenges, and New Perspectives (2016, background): “In recent years, much progress has been made in the development of process-based model evaluation methods that much better extract information from the available data (Gupta et al., 2008; Vrugt and Sadegh, 2013).”
A philosophical basis for hydrological uncertainty (2016, methodology): “…by (i) estimating a model error distribution directly from data (e.g. Wilkinson et al. 2011, Sikorska et al. 2014) and then going on to test such models using an independent data source, or (ii) by performing both steps simultaneously (e.g. Vrugt and Sadegh 2013, Brynjarsdóttir and O’Hagan 2014).”
Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation (2016, methodology): “…and includes a detailed convergence analysis of the sampled chains, post-processing tools, and recent developments in di-170 agnostic model evaluation using approximate Bayesian computation (Vrugt and Sadegh, 2013; Sadegh and Vrugt, 2014).172 The remainder of this paper is organized as follows.”
The evolution of process-based hydrologic models: Historical challenges and the collective quest for physical realism. (2017, unknown): “A key path forward is to identify different signatures from the data that can be used to improve parameter values in different parts of the model (Gupta et al., 2008; Yilmaz et al., 2008; Pokhrel et al., 2012; Vrugt and Sadegh, 2013; Rakovec et al., 2015).”
Multivariate Copula Analysis Toolbox (MvCAT): Describing dependence and underlying uncertainty using a Bayesian framework (2017, methodology): “A model hypothesis,M, simulates the system response, ~ Y, given a d31 vector of parameter values, h5fh1; h2; ; hdg, and forcing, ~I [Vrugt and Sadegh, 2013],”
Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication (2016, methodology): “In the rainfall–runoff modelling case it has been suggested that the use of summary statistics for model evaluation, such as the flow duration curve, might be more robust to error in this sense (e.g. Westerberg et al. 2011b, Vrugt and Sadegh 2013).”