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This analysis results a minimal, finite-dimensional, affine parameterization of the variance error.
We characterize the set of all functions Φ for which the variance error remains constant.
Recently, the NSO/SOLIS team developed variance (error) maps that represent uncertainties in magnetic flux synoptic charts.
The variance error term dominates when dealing with short and fat data sets, which is typical in batch processes.
The technique uses a decoupling procedure optimized by a minimum variance error to estimate the inductance and resistance of the motor.
It is shown that the set of all Φ for which the variance error remains constant can be characterized by the solutions of a Nevanlinna Pick interpolation problem.
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The uncertainties in the estimated in heat transfer coefficient are calculated using Bias and Variance errors.
If so, errors will propagate on subsequent turbo iterations since the decoding algorithms are sensitive to the variance errors.
However, in ensuing works it was found that damping matrices identified from the method had unexpected forms and showed traces of large variance errors.
We present here a slightly improved version of the original method and, more importantly, we thoroughly analyze the method in terms of bias and variance errors.
The modal parameters of a structure that are estimated from ambient vibration measurements are always subject to bias and variance errors.
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