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Because they are usually modeled as random errors, region wide estimates of error are very low.
The CG-EGA classifies errors in three clinically relevant categories accurate readings (region A), benign errors (region B), and erroneous readings (regions C, D, and E with clinically relevant classification cutoffs based on the potential error in treatment given the sensor inaccuracy.
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Figure 3 shows an example of an error region.
The white region in Figure 7b represents the no-error region, because all errors are corrected by a channel decoder with the received parity bits from an encoder.
Probabilistic algorithms use a region, an "error region" which is usually an ellipse or a rectangle to match the given point.
Abel and Smith proposed an explicit solution that can achieve the Cramer-Rao Lower Bound (CRLB) in the small error region [52].
We give a computable bound which ensures the tightness of the SDR if the radius of the error region is less than this bound.
For rough estimation, E = 0.2, which corresponds to a normalized eigenvalue of 1 ± 0.2, yields an error of approximately 1 dB so that the error region of E < 0.2 might be sufficiently accurate in practical application.
The improved importance sampling (IIS) technique proposed in [3], on the other hand, shifts the mean of the actual PDF of the input random variables (RVs) directly towards the error region.
However, we observe that the low error region, for both initial cell densities, is very wide and there is no such unique minimum.
Closer inspection of the low error region revealed that the SVM method clearly recovered more true interactions in this realm than the Bayesian model (Fig. 1B, FPR <0.01, i.e. ∼50 false positives).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com