Multiresponse multilayer vadose zone model calibration using Markov chain Monte Carlo simulation and field water retention data

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Modeling Soil Processes: Review, Key Challenges, and New Perspectives (2016, methodology): “Outside the BMA context, parameter-related uncertainty after calibration can be quantified through classical Bayesian inference and by Markov chain Monte Carlo (MCMC) simulation techniques (e.g., Vrugt et al., 2009b; Wohling and Vrugt, 2011).”
Inverse modeling of dynamic nonequilibrium in water flow with an effective approach (2012, methodology): “By assuming independent and normally distributed errors with zero mean, the log likelihood is equal to the one used by Wöhling and Vrugt [2011]:”
Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation (2016, background): “…Shafii et al., 2014), physics (Dura et al., 2014; Horowitz et al., 2012; Toyli et al., 2012; Kirby et136 al., 2013; Yale et al., 2013; Krayer et al., 2014), psychology (Turner and Sederberg, 2012), soil hydrology (Wöhling and Vrugt, 2011), and transporta-138 tion engineering (Kow et al., 2012).”