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These hourly results were processed to obtain annual heat gain intensities for each parametric case for all three modes of heat transfer.
This proves that the optimal sampling rate for each parametric method guarantees the best performance with respect to accuracy and computational time.
For each parametric shaping function, we obtained the 3D mesh curves according to the objective measures as a function of the various values of α and β when γ is fixed as illustrated in Figs. 4 and 5 at which the packet loss rate was assumed to be 10%%.
This also allowed to extract comparable parameter estimates (fits) for each parametric modulator.
The M-step (maximization) estimates maximum likelihood parameters θ c for each parametric component f c individually, using the expected memberships as hidden variables that decouple the model.
The full model presented in (2) contains a predictor representing the average effect of experimental condition j plus an additional parameter for each parametric regressor k considered.
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The between-subject average regression functions and standard error of the mean were then computed and are displayed in Figure 3 for each ROI and each parametric regressor (Fig. 3 side panels).
With respect to this, we note that it is true only for each single parametric perturbation, i.e., the inhibitory effect may not be active in the dynamic changes of multiple parameters as observed in our multi-parametric approach.
For each outcome, parametric survival and frailty distributions were fitted and results reported for the model preferred by the AIC selection criteria (Burnham and Anderson, 2004).
Gleason scores for each multi-parametric MRI pattern were also evaluated.
Additionally, Gleason scores for each multi-parametric MRI pattern were evaluated.
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