We can set the threshold $r$ for the likelihood to a suitable value, such that the total probability of the region of rejection $R_{r}$ is below a given level of error, for example, $P_{h}(R) < 0.05$.
The optimization is based on the idea of keeping an error of interest (dispersion, phase or group velocity errors) below a given threshold for a wavenumber interval as large as possible.
The stability of the numerical solution is maintained and the accuracy is controlled by keeping the local error below a given threshold using PI-control.
Furthermore, an algorithm is presented to do the splitting such as to keep the linearization error below a given threshold with a minimum number of components.
In general, the typical objective function for evaluating the reproducibility of an experimentally measured time-series for a parameter set is the total relative error, E. The parameter set is then estimated when the total relative error falls below a given threshold.
Further bits are requested until the estimated bit error rate drops below a given threshold, then each coefficient value is reconstructed using a minimum mean square error estimator, and the IDCT returns the decoded Wyner-Ziv frame.
In contrast, in Model 1, our method failed to estimate the parameter values, due to the lack of an error function below a given threshold, while the standard method succeeded with the broad parameter space.
The SI is used in the turbo decoder, along with WZ parity bits requested from feedback channel, in order to reconstruct the decoded WZ frames In this paper, the turbo decoder stops requesting more bits if the bitplane bit error rate is below a given threshold equal to Figure 1 DVC architecture.
Conversely, a given standard error implies a minimum sample size.
DECA is designed to keep the reconstruction error of the monitored process below a given fraction of its actual value.
