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Let us examine both approximations, shortly indicated in the following as rms and LLNL solutions, and let us perform a kinematical analysis of the expected errors with respect to the exact 3D migration.
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The basic idea is to use Monte Carlo to produce values of the error of a parametric regression estimate for randomly chosen designs and randomly chosen parameters; then, using this data, nonparametric regression is used to estimate the design for which the maximal expected error with respect to all possible parameter values is minimal.
When we investigated the expected error with temporally offset inputs, we found that activation of inputs in the preferred direction produced a large difference between the IV plot of combined excitatory and inhibitory activation and the expected arithmetic sum of the individual responses and, correspondingly, in RPC recordings (Fig. 7 B, top).
Darker regions correlate with higher expected errors.
Notice that the statistical procedure associates expected error rates with a decision to reject or accept.
As expected, error rates increased with K for each classifier, since the baseline error increased with K; in each instance, classifiers improved upon random classification.
The approximation of the expected error rate associated with plug-in Bayes discriminant function is derived.
The error budget for the three radio-metric observables was consolidated by comparing the statistical properties of the data set with the expected error models.
These tests were run using a Visual Basic routine developed by N. Barve [ 85], with an expected error rate of E = 1% [ 82].
Six mismatches were tolerated as recommended by the Corona documentation, and reads with an expected error rate (Equation 1) greater than 6 were discarded prior to matching.
The rest 6.2 kb represents 0.1% of the sequenced genome and within the expected error level when sizing fragments with fluorescent intensity.
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