Sentence examples for discrepancy from predicted values from inspiring English sources

A high ppp-value indicates that the model generates replicate datasets that show consistently greater discrepancy from predicted values than does the observed dataset.

For example, ppp-values close to zero indicate that most of the replicated datasets were less extreme than the observed datasets, and hence the model shows less discrepancy from predicted values, compared to the real data.

In the 1850s, Urbain Le Verrier made detailed calculations of Mercury's orbit and found a small discrepancy in the planet's perihelion precession from predicted values.

Figure 9 shows the discrepancies of the predicted values from the experimental ones in percentage, for the test data.

Several researchers, e.g. [21, 24, 28, 38] reported significant discrepancies between the predicted values calculated using Eq. (1) and the experimentally measured axial soil resistance.

Open image in new window Fig. 8 Surface roughness for test data Open image in new window Fig. 9 The discrepancy of the predicted value from the experimental in percentage, for test data.

Open image in new window Fig. 3 MSE during training Open image in new window Fig. 4 Adaptive mu during training Open image in new window Fig. 5 Experimental values of surface roughness versus the NN predicted ones Open image in new window Fig. 6 The discrepancy of the predicted value from the experimental in percentage, for test data.

The different approaches to basal area and volume estimation of Eucalyptus stands were evaluated by comparing the basic statistics of the predicted maps (mean and standard deviation) with the estimates obtained from the forest inventory, and through the discrepancies between observed and predicted values in the fitting and validation datasets.

Furthermore, reaction rate sensitivity analysis as well as binary diffusion coefficient sensitivity analysis are conducted to identify the possible sources of discrepancy between the experimental and predicted values.

RMS error indicates the discrepancy between the measured and predicted values.

In essence, there is need for model upscaling to account for the discrepancies between the measured and predicted values.