Sentence examples for mean errors compared from inspiring English sources

In both perseveration and learning phases, the MD lesion group made greater mean errors compared to control and sham rats.

A numerical analysis of these errors has shown that vibrations associated with swallowing have distinctive mean square errors compared to the errors obtained for the vibrations associated with other phenomena (e.g., vocalization or noise).

The results demonstrate that the recursive digital filters designed by the proposed approach have significantly reduced the relative root mean square errors compared with the conventional methods and have the required zero phase response.

(v) Close-in echoes: tracking of closely spaced signals with the proper multiple path signal model may lead to increased mean square errors compared to the case when tracking is based on a single-path model [29].

A frequentist hierarchical model with components of variance could also be used and also borrows information; however frequentist point estimates of the variance may have large mean square errors compared to Bayesian estimates [ 3].

For this model, the mean square error compared to the theoretical power level is 2.5 dB.

Lastly, the fractional additivity model generally yielded similar or lower root mean squared error compared to the other models.

The best model reduced the error up to 79% in terms of mean absolute error compared with error in water balance equation when conventional (with no correction coefficients) water balance analysis was conducted.

A modified powered exponential correlation function is introduced, which in conjunction with a gradient based space-filling design resulted in an averaged 8 times smaller mean squared error compared to other correlation design combinations.

In each frequency bin, the best possible permutation is found by considering all possible permutations of the estimated sources and by selecting the one that leads to the smallest mean square error compared to the true source signals in that bin.

The analysis shows the robustness of the STM to various spatial distributions of measurements and its fast convergence and low residual root mean square error compared to the inverse distance weighted (IDW) method, the inverse distance square weighted method (IDW2), the Kriging method, and the location estimation-based method (LIvE).