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This paper develops a heuristic methodology for designing precise sensor/measurement networks for linear material flow circuits based on the general principle of variance reduction through data reconciliation.
The Sobol method (Sobol 1993, 2001) is a variance-based method that performs a quantitative analysis of model sensitivity based on the principle of variance decomposition.
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These situations are analyzed by application of the principle of least variance in conjunction with the R-integrals.
The principle of least variance is applied to evaluate the reliability of the design conditions of the Runyang cable-stayed bridge.
The principle of inverse-variance weighting is often used.
Results are applicable to any Kalman-type coupled input-state estimator derived using the principles of minimum-variance unbiased estimation.
The third method uses the principle that the variance of the variance in allele lengths is expected to be larger in a constant-sized than in a growing population, assuming that the loci follow an SMM.
DNA methylation values were adjusted for technical and biological covariates by using the first five principle components that explained 46%% of variance in the data.
These categorical numbers reflect directionality, but they do not account for the fact that the nine principle components explained different proportions of variance (Additional file 2: Figure S2).
A GP prediction of a given compound-target combination is a Gaussian distribution whose variance defines intervals of confidence: in principle, this variance measures the distance of the compound-target pair to the training set.
Changing the nonlinear link function of the model (from an exponential to another type of nonlinearity, see Eq. 1 and Section 2.4.2) could, in principle, help to increase the amount of variance in the response described by the model.
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