Sentence examples for measures the discrepancy from inspiring English sources

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

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.

For these six measures the discrepancy is the greatest.

The root mean square error of approximation measures the discrepancy between the model and the observed covariance matrix and is expressed per degree of freedom, thus taking into account the complexity of the model.

The first summation measures the discrepancy between PBs and KBs by adding the distance from each KB to the closest PB, and the second summation performs the same operation, but includes the distance from each PB to the closest KB.

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

We will do this based on simulations and will measure the discrepancy using a symmetrized version of the Kullback-Leibler divergence (the so-called Jensen-Shannon divergence (JSdiv)).

In order to stabilize these types of systems in this contribution the use of a generalized distance measure, the discrepancy, is proposed.

We introduce another metric called the global variance of link (GVL) to measure the discrepancy among links, that is, how well nodes are uniformly distributed.

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.

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.