Development of a formal likelihood function for improved Bayesian inference of ephemeral catchments

By Tyler Jon Smith, Ashish Sharma, Lucy A. Marshall, Rajeshwar Mehrotra, Scott Anthony Sisson. 2010

98 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/cb84bc5598542f6c0d7f89bc3dbfac91fde2c9a2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Comparison of joint versus postprocessor approaches for hydrological uncertainty estimation accounting for error autocorrelation and heteroscedasticity (2014, background): “For example, calibration and predictive uncertainty quantification are much more challenging in ephemeral catchments [Ye et al., 2008; Smith et al., 2010].”
An adaptive sparse‐grid high‐order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling (2013, methodology): “The informal likelihood function is designed carefully to implicitly account for errors in measurements, model inputs, and model structure and to avoid overfitting to measurement data [Beven and Binley, 1992; Smith et al., 2008; Schoups and Vrugt, 2010; Smith et al., 2010)].”
Toward diagnostic model calibration and evaluation: Approximate Bayesian computation (2013, background): “In the past years, various improvements to equation (7) have been proposed to handle nontraditional residual distributions [Schoups and Vrugt, 2010; Smith et al., 2010].”
Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation (2016, background): “Various improvements to Equation (6) have been proposed to handle nontraditional error residual distributions (Schoups and84 Vrugt, 2010a; Smith et al., 2010).”
Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation (2013, methodology): “…for measuring the closeness between the model simulations (predictions) and the corresponding data (Ibbitt and O’Donnell, 1974; Sorooshian and Dracup, 1980; Kucz-era, 1983a; Bates and Campbell, 2001; Kavetski et al., 2006a; Marshall et al., 2007; Schoups and Vrugt, 2010a; Smith et al., 2010).”