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Measuring the Predictive Capability of Computational Models: Principles and Methods, Issues and Illustrations

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References

1979Statistics for Experimenters6,750 cited
1965Conjectures and Refutations: The Growth of Scientific Knowledge.4,189 cited
1981On The Quantitative Definition of Risk3,099 cited
1994Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences3,016 cited
2000Geostatistics: Modeling Spatial Uncertainty2,880 cited
1996Different methods are needed to propagate ignorance and variability621 cited
1999Statistical Validation of Engineering and Scientific Models: Background102 cited

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2007Validated Model? Not So Fast. The Need for Model "Conditioning" as an Essential Addendum to Model Validation

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20 cited

2008A General Strategy for Physics-Based Model Validation Illustrated with Earthquake Phenomenology, Atmospheric Radiative Transfer, and Computational Fluid Dynamics15 cited

2008Type X and Y Errors and Data & Model Conditioning for Systematic Uncertainty in Model Calibration, Validation, and Extrapolation 111 cited