Based on a given threshold, two groups are defined – predicted variants and predicted non-variants.
In contrast, the receiver operating curve (ROC) provides a global assessment of diagnostic accuracy without any focus on a given threshold.
The positive predictive value (PPV) estimates the accuracy of the model by comparing the number of people identified by the model as being likely to experience a re-admission (based on a given threshold of risk) with the number in this group who went on to experience a re-admission.
It is generally considered in literature that the secondary users can only transmit if the primary network is inactive or if the secondary users are located outside a keep-out region surrounding the primary transmitter, or equivalently, if the secondary users generate an interference inferior to a given threshold on a so called protection contour surrounding the primary transmitter [7, 8].
In the ROC curve, for a given threshold on the log odds ratio, TP is the fraction of the true exon from TWINSCAN with the log odds score greater than the threshold and FP is the fraction of the false exon from TWINSCAN with the log odds score greater than the threshold.
It turns out that the minimum sum-correlation is achieved by block-size allocation policies that lie in a set composed of all block-sizes that are inferior or equal to a given threshold depending explicitly on the system's parameters: the number of subcarriers, the number of users, and the cyclic prefix length.
For the same voxel or region, we also measure the temporal coherence of its fluctuations to other voxels or regions, based on exceeding a given threshold, Θ, for zero lag correlation, establishing functional connectivity between pairs of neuronal populations.
That squeeze may demand even bolder steps to tap the mountain of wealth in private hands, such as imposing a tax on all fortunes over a given threshold.
Therefore, it aims to increase specificity at the cost of sensitivity and provides higher-confidence orthologous gene relationships than those based simply on sequence similarity below a given threshold.
Another type of solvers employ a thresholding step to iteratively refine the recovered support, i.e. the selection/rejection of an entry at each step, is decided by whether the value of a certain function dependent on this entry falls below a given threshold.
For a given eruption scenario, the probability P M at coordinates x,y is calculated based on the number of times a given threshold of accumulation is reached and divided by the total number of runs N R : P_{M} x,y) = frac{sum_{i=1}^{N_{R}}n_{i}}{N_{R}} n_{i} = left{ begin{array}{l l} 1 & quad text{if (M_{i} x,y) geq) threshold (mid) eruption} 0 & quad text{otherwise.} end{array} right.
