Sentence examples for different variance parameters from inspiring English sources
For n.comp = 2 or 3 estimation of different variance parameters for each component is allowed.
Different variance parameters and a range of model violations are studied using simulation and application.
Using LRT to include or exclude random effects parameters is generally unreliable, as suggested by Wählby et al. For this reason, models with different numbers of variance parameters should also not be compared using LRT.
Table 5 Influence of reverse rhythm formation parameters' synthetic variation on oil recovery under different variance coefficients Variance coefficient Oil recovery prediction Difference in recovery prediction Neglect parameters' variation Consider parameters' variation 0 43.86 45.72 1.86 0.3 44.42 46.62 2.20 0.5 43.28 45.60 2.32 0.7 40.24 43.67 3.43 0.9 31.32 32.97 1.65.
Relationship between water cut and oil recovery under different variance coefficients when considering reservoir parameters' variation is shown in Fig. 5a.
Significances are based on 10100 permutations evaluating whether fixation indices were different from a null distribution of variance parameters assuming samples were drawn from randomly chosen species.
Figure 5b and Table 4 show the variability of the influence degree of reservoir parameters' alteration on development effect under different variance coefficients.
The influence of reservoir parameters' variation on development effect under different variance coefficients is discussed in positive rhythm formation and reverse rhythm formation, respectively.
Data groups were compared by analysis of variance; parameters whose variances were determined to be significantly different were then compared by Student's t-test.
Zero mean Gaussian noise of different variance was added and the values of the similarity measure for different candidate transformation parameters calculated.
The below four Bayesian modelling approaches are modifications of the unrestricted heterogeneous variance model that apply different parameter value constraints or moderately informative prior distributions to optimize the estimation of the between-trial variance parameters.
