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portada Approaches to Highly Parameterized Inversion: A Guide to Using PEST for Model-Parameter and Predictive-Uncertainty Analysis
Type
Physical Book
Author
Publisher
Language
English
Pages
78
Format
Paperback
Dimensions
28x21.6x0.4 cm
Weight
0.20 kg.
ISBN13
9781500299989
Categories

Approaches to Highly Parameterized Inversion: A Guide to Using PEST for Model-Parameter and Predictive-Uncertainty Analysis

Randall J. Hunt (Author) · John E. Doherty (Author) · Matthew J. Tonkin (Author) · Createspace · Paperback

Approaches to Highly Parameterized Inversion: A Guide to Using PEST for Model-Parameter and Predictive-Uncertainty Analysis - Hunt, Randall J. ; Tonkin, Matthew J. ; Doherty, John E.

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Synopsis "Approaches to Highly Parameterized Inversion: A Guide to Using PEST for Model-Parameter and Predictive-Uncertainty Analysis"

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 uncer- tainty analysis with regard to models can be very computa- tionally demanding, due in part to complex constraints on parameters that arise from expert knowledge of system proper- ties 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).

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