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The largest uncertainty originates from the beam source itself.
In the first stage, uncertainty originates from data that are sparse, noisy, ambiguous, or derived from heterogeneous samples.
Another component of uncertainty originates from vagueness in the definition of the measurand, and is known as "definitional uncertainty" (Mari and Giordani 2013).
The depth uncertainty originates from the volumetric nature of well-logging that each record belongs to a volume of investigation, around the logging tool.
A combined strategy of model predictive control (MPC) and least squares online parameter estimation for the control of the hypnotic depth, measured by the Bispectral Index (BIS), under uncertainty is presented, where the uncertainty originates from patient variability.
The uncertainty originates from forecasting errors that are superimposed on simulated time series of load and wind generation, and this uncertainty is simulated as stochastic, normally distributed vectors of growing variance.
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As a result, calculated assigned values have an uncertainty originating from the testing conditions of laboratories and other factors.
However, model predictions have an inherent uncertainty (i.e. prediction uncertainty) originating from uncertainty in input data, model parameters, model structure, boundary conditions and software.
To propagate the uncertainty originating from the parameter uncertainty to the model output, the Generalized Likelihood Uncertainty Estimation (GLUE) method is used.
The goal herein is to understand where modeling errors and uncertainty originate from, and obtain model predictions that are statistically consistent with their respective measurements.
Namely, the uncertainty originating from the models was assessed by Monte Carlo simulations propagating the error equal to the variance of the model residuals and propagating the error originating from the model coefficients' calculation.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com