Sentence examples for prediction exceeded from inspiring English sources

Second, for BayesR, the accuracy of prediction exceeded 0.61 at 20 000 iterations, and did not improve with a larger number of iterations, as shown in Figure  6.

Using BayesC with π = 0.996 for 50 K SNPs, the correlation between phenotype and whole-genome prediction exceeded 0.97 at burn-in, 0.980 after 280 post-burn-in iterations, 0.983 after 6280 post-burn-in iterations, and then it failed to increase further.

Predictions falling outside were considered false positives (FP) and sequences having no prediction exceeding the threshold were counted as false negatives (FN).

This allows checking the expected accuracy when gradient predictions exceed the initial solvent concentrations tested in the training set.

Our simulations, based on a large set of long-term TCP sessions, show that 70% of their predictions exceed the boundaries of TCP-Friendliness, thus questioning their use in the design of new TCP-Friendly transport protocols.

Table 1 shows that, for all knockouts, the frequency of correct predictions exceeds 33%.

Comparison of mass predictions from volumetric reconstructions of near-complete skeletons of Apatosaurus and Giraffatitan (figure 2) to the mean scaling equation masses, produces a qualitatively similar result: scaling equation mass predictions exceed those of our maximal models (figure 2 c, d).

In this case the classification accuracy for both prediction categories exceeded 75%%, while the number of predictions remained substantial.

Regardless of the regression used, the r2 of the prediction models exceeded 0.70 for Kd-17β-estradiol and Kd-estriol, but r2 was below 0.52 for Kd-equol.

In Figure 7, we see that the number of handovers to Wi-Fi in the prediction model exceeds the original model at around 115 s; subsequently, in Figure 8, the sample scores in the prediction model are higher at around 115 s, while the WiMAX values are substantially lower in Figure 9.

Traditional bankruptcy prediction models, designed using classification or regression techniques, achieve short-term performances (1 year) that are fairly good, but that often worsen when the prediction horizon exceeds 1 year.