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This observation becomes even more clear by considering Figures 6 and 7. There, we compare the previously introduced "average energy points" and "energy variance points" to the analytically determined expected free energy and corresponding variance from [ 21], respectively.
Obviously, we can also compute the corresponding "average energy points" A v E P (S, e n e r g y ) : = (n, μ n : = 1 card (S n ) ∑ s ∈ S n e n e r g y (s ) ) ∣ S n ≠ ∅ and the corresponding "energy variance points" V a r E P (S, e n e r g y ) : = { (n, σ n 2 : = 1 card (S n ) ∑ s ∈ S n (μ n − e n e r g y (s ) ) 2 ∣ S n ≠ ∅ }, respectively.
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The shape of data is used as an organising principle and the attribute of geometrical symmetry of cost variance point clouds used for the quantification of cost uncertainty.
If not, differences in sequencing depth could contribute to differences in variance (point #1).
A non-linear model of regression was used to calculate a weighted R which takes into account the uneven variance between points across the graph, ensuring that all points contributed equally.
For the (co variance estimates, the sigma-point approach consistently outperforms linearization approach for increasing parameter variance level.
We decrease the ω values iteratively and compute the variances of points between these two lines.
It is shown that the optimum input distribution varies with the variance of pointing jitter σ and laser beam divergence angle w0 and the normalized intensity threshold IT.
Amiri et al. (2014) developed a probabilistic neural network (PNN -based PNN -basedto estimate the variance change procedurea univariato procestimate normal qualithecharacteristic.
**I=34%, P=0.11 for heterogeneity, variance in point estimates, from 0.25 to 2.97.
†Wide variance of point estimates across studies or large heterogeneity between trials (I>50%).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com