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Uncertainty in a monthly water balance model using the generalized likelihood uncertainty estimation methodology
(2015)
Hydrological models are simplified representations of natural processes and subject to errors. Uncertainty bounds are a commonly used way to assess the impact of an input or model architecture uncertainty in model outputs. ...
Uncertainty in a monthly water balance model using the generalized likelihood uncertainty estimation methodology
(2015)
Hydrological models are simplified representations of natural processes and subject to errors. Uncertainty bounds are a commonly used way to assess the impact of an input or model architecture uncertainty in model outputs. ...
On the robustness of structural risk optimization with respect to epistemic uncertainties.
(Redding, 2012)
In the context of structural design, risk optimization allows one to find a proper point of balance between the concurrent goals of economy and safety. Risk optimization involves the minimization of total expected costs, ...
A Study on the Foundations of the Occurrence of Errors in Subjective Measurements
(Los Angeles, 2002-03)
Background
Due to the increasing need to better understand organizational elements, subjective measurements are gaining
more and more space.
Problem: What are the grounds to claim that subjective measurements allow ...
Uncertainty in a monthly water balance model using the generalized likelihood uncertainty estimation methodology
(2015)
Hydrological models are simplified representations of natural processes and subject to errors. Uncertainty bounds are a commonly used way to assess the impact of an input or model architecture uncertainty in model outputs. ...
Signal-to-noise ratio constrained feedback control: discrete-time robust stability analysis
(IET Control Theory and Applications, 2020)
Revisão da literatura sobre modelos de Programação por Metas determinística e sob incerteza
(Associação Brasileira de Engenharia de Produção, 2014)
The goal of this study was to identify the principal goal programming (GP) models in existence, analyzing the advantages and disadvantages of each model when used to treat real situations involving large, complex problems. ...
Heteroscedastic controlled calibration model applied to analytical chemistry
(JOHN WILEY & SONS LTD, 2010)
In chemical analyses performed by laboratories, one faces the problem of determining the concentration of a chemical element in a sample. In practice, one deals with the problem using the so-called linear calibration model, ...
Revisão da literatura sobre modelos de Programação por Metas determinística e sob incerteza
(Associação Brasileira de Engenharia de Produção, 2015)