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Approaches to highly parameterized inversion: A guide to using PEST for model-parameter and predictive-uncertainty analysis

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Approaches to highly parameterized inversion: A guide to using PEST for model-parameter and predictive-uncertainty analysis; 2010; SIR; 2010-5211; Doherty, John E.; Hunt, Randall J.; Tonkin, Matthew J.

Summary

Analysis of the uncertainty associated with parameters used by a numerical model, and with predictions that depend on those parameters, is fundamental to the use of modeling in support of decisionmaking. Unfortunately, predictive uncertainty analysis with regard to models can be very computationally demanding, due in part to complex constraints on parameters that arise from expert knowledge of system properties on the one hand (knowledge constraints) and from the necessity for the model parameters to assume values that allow the model to reproduce historical system behavior on the other hand (calibration constraints). Enforcement of knowledge and calibration constraints on parameters used by a model does not eliminate the uncertainty [...]

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Harvested on Mon Jul 21 11:28:23 MDT 2014 from MODS XML Service

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local-index unknown sir20105211
local-pk unknown 70005324
doi http://www.loc.gov/standards/mods/mods-outline-3-5.html#identifier doi:10.3133/sir20105211
series unknown Scientific Investigations Report

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journalScientific Investigations Report
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typePublication Place
valueReston, VA
languageEnglish
citationTypeReport

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