"measure the discrepancy" is correct and usable in written English.
You can use it when you want to emphasize the need to accurately measure the difference or distance between two or more items. For example, "Before making any changes to the room's layout, we need to measure the discrepancy between the walls and the furniture."
In order to stabilize these types of systems in this contribution the use of a generalized distance measure, the discrepancy, is proposed.
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)).
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.
One common approach to anomaly detection makes use of unsupervised learning: we learn a baseline model of the phenomenon of interest, and then measure the discrepancy of subsequent data from the baseline.
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.
Model evaluations were based on Χ2 statistics that measure the discrepancy between observed and model-implied covariance matrices.
The parameter estimation is derived from adequate objective functions measuring the discrepancy between the experimental and numerical modal data.
The performance of a network is quantitatively assessed through a cost function that measures the discrepancy between the extrapolation and the true concentration fields.
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.
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.
