In channel coding and estimation (rows 8 9 and 16), the signal model is normally deterministic.
Remark 5.3 According to Theorem 5.2, we may conclude that the generalized hierarchical teaching model is normally better than the generalized traditional teaching model.
Consequently, the reduced-order model is normally smaller and more accurate on a broader parameter range when multiple expansion points are used.
Thus, the MR-FDPF model is normally limited to simulate the small cell scenarios, such as the indoor radio propagation scenarios.
In global SA, a linear regression model is normally applied to sampled-based solutions by stepwise manners, and the relative importance of variables is examined by sensitivity indexes.
For the purpose of comparison, additional generalized additive models (gam) were parameterized, because the prediction accuracy of the mean population model of mixed models is normally a little lower.
In Section 5, we demonstrate the applications of these results, we show that the generalized hierarchical teaching model is normally better than the generalized traditional teaching model (see Remark 5.3), and we point out the significance of quasi-log concavity in the analysis of variance and the generalized traditional teaching model.
Based on the monotonicity of the interval function ({operatorname{Var}^{ [ gamma ] }}{X _{ [ {{a},{b}} ] }}), they show that the hierarchical teaching model is normally better than the traditional teaching model under the hypothesis that {X _{I}}subset X sim{N_{k}} ( {mu,sigma} ),quad k>1.
It is known that the resulting solution of FBA especially when applied to genome scale models is normally not unique [ 65].
However, it is assumed that the prognostic index resulting from the model is Normally distributed (although this is an approximation in the case of a non-continuous prognostic index).
