Sentence examples for measuring the discrepancy from inspiring English sources

The parameter estimation is derived from adequate objective functions measuring the discrepancy between the experimental and numerical modal data.

From the modal information extracted from two specimens with different geometries, the procedure allows the simultaneous estimation of the skin and core constitutive parameters through adequate objective functions measuring the discrepancy between the experimental data and the numerical predictions.

A more general perspective, first developed by Wald (1950), is provided by measuring the discrepancy between the predictions of the hypothesis and the actual data in terms of a loss function.

A distance function is useful in measuring the discrepancy between two rankings.

For instance, by measuring the discrepancy between PPI networks of healthy and diseased individuals it is possible to predict disease outbreak and progression [ 8].

In the psychophysical literature, ventriloquism effect and aftereffect are assessed by measuring the discrepancy between the perceived sound location and the actual sound location (perceptual shift).

Similarly, by measuring the discrepancies between PPI networks of healthy and sickened individuals, interactions that are involved in disease outbreak and progression are determined [ 4].

The performance of a network is quantitatively assessed through a cost function that measures the discrepancy between the extrapolation and the true concentration fields.

Three metrics that measured the quality of a pose estimate were evaluated and a joint classifier was constructed by combining the most discriminative quality metric with a fourth metric that measured the discrepancy between two independent pose estimates.

There are two natural densities associated with this problem: the discrepancy densityρH, given byρH= liminfr→1−inff⁡∫D 0,r)((1−|z|2)|f z)|−1)2dA(z)1−|z|2∫D(0,r dA z 1−|z|2 which measures the discrepancy in optimal approximation of (1−|z|2)−1 with the modulus of polynomials f, and its relative, the tight discrepancy densityρ⁎H, which will trivially satisfy ρH≤ρ⁎H.

We used the energetic yield in order to quantify the robustness of a protein, and to detect sensitive regions and sensitive residues in the protein, whereas we adopted the energetic relative entropy to measure the discrepancy between two potential energy distributions.