Efficient nonlinear predictive error variance for highly parameterized models

56 citations recorded by the source corpus.

How researchers see this paper

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

Real‐time groundwater flow modeling with the Ensemble Kalman Filter: Joint estimation of states and parameters and the filter inbreeding problem (2008, background): “…captures some of the details at smaller scales, namely those which are important for groundwater flow and transport predictions, and also estimates an uncertainty associated with the prediction [e.g., Hernandez et al., 2003; Vermeulen et al., 2004; Tonkin and Doherty, 2005; Tonkin et al., 2007].”
Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors (2010, background): “Kavetski et al. [2006b] and Tonkin et al. [2007] further discuss the significance of the covariance/Hessian matrix and its eigenvalues for the estimation of nonlinear models.”
Calibration‐constrained Monte Carlo analysis of highly parameterized models using subspace techniques (2009, background): “Rigorous theory for calculating objective function thresholds corresponding to various levels of calibration confidence is in fact available [Vecchia and Cooley, 1987; Tonkin et al., 2007].”