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Red line indicates the distribution when disturbed.
Figure 4b shows the distribution when (K = 1).
For almost all the distribution, when income raises, the probability increases.
Figure 1 more clearly demonstrates how m and ν influence the centrality and shape of the distribution when λ=2.
Unlike the random forest, the maximum entropy model is designed to work with sub-optimum sample size as it predicts the distribution when it is under-sampled as in most signal processing problems [38].
Output values of the network are shown in Figure 6(a) which represents the output values distribution when pedestrians are classified, while (b) is the distribution when contours of objects that are not pedestrians are analyzed.
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It is demonstrated that there is partial overlap in the distributions when only variability in the model parameters is evaluated and there is substantial overlap when variability is included in both the model and initial seed distribution parameters.
Pearson Chi-squared tests were used to establish the difference between the distributions when it was not possible to analyse differences in medians.
The results demonstrate that the proposed method can accurately capture the stress distribution when the amplitude of the imperfection is small.
Their phantom and simulation studies showed significant improvements in the reconstruction of the fluorescence distribution when the structural prior was used.
The figure shows that decreasing the shaping parameter produces a shaper peak and that the bi-kappa distribution approaches the AWG distribution when the tuning parameter is increased.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com