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Figure 2 illustrates the distribution of errors in the test set.
In this paper we address the assumptions about the distribution of errors made by voxel-based morphometry.
However, the distribution of errors was skewed towards negative values, meaning that TEW was likely to underestimate the activity.
The distribution of errors for the sites, and the geometry of the sites, determine the performance of the proposed method.
As the prediction time leap t d increases, the distribution of errors starts to flatten out, because the error increases.
Figure 8 shows the distribution of errors in the planar strain for increasing tilt, (alpha), around the (Omega _1 -axis.
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Assume the distribution of error monotonically decreases away from the maximum.
Finally, the distribution of error was set to be either random or non-random.
We investigated the distribution of error measurements on a per-tree basis.
The distribution of error, as illustrated in Figure 1, does not fit a stochastic model, for any error type.
The distribution of error values, if analysed, will identify gross errors in that case, i.e. clearly isolated values.
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CEO of Professional Science Editing for Scientists @ prosciediting.com