Sentence examples for accurate confidence from inspiring English sources

Wealthcare's simulation technology is able to analyze thousands of randomized market outcomes, testing best and worst case scenarios and reporting back an accurate confidence score to the client.

For example, Efron and Tibshirani (1993) stated that a minimum of approximately 1000 bootstrap re-samples was sufficient to obtain accurate confidence interval estimates.

As mentioned in Section 4.2, posterior probability estimation using rich word lattices is often used in large vocabulary applications, where it usually provides accurate confidence measures, although it is computationally expensive.

In addition, a bootstrap based statistical approach developed in the wavelet domain is proposed and shown to enable the practical computation of accurate confidence intervals for multifractal attributes from a given image.

The results presented in this study recommend using bootstrapping in development of more accurate confidence intervals for risk curves in injury biomechanics, which consequently will lead to better regulations and safer vehicle designs.

These simulations show that the proposed method improves significantly the statistical properties of the estimator and provides a more accurate confidence region around the estimated parameters, in comparison with classical methods.

Furthermore, we developed an efficient algorithm to determine accurate flux confidence intervals and demonstrated that confidence intervals obtained with this method closely approximate true flux uncertainty.

A simulation study procedure is implemented to evaluate and compare the accuracy of the approach to two already established methods, showing that the TI approximation produces accurate empirical confidence levels which are reasonably close to the nominal confidence level.

Upper and lower 95% limits of the ΔΔ Ct value were then used to give a more accurate 95% confidence limit on the copy number estimate, given as [2∧−ΔΔCt High− 2∧−ΔΔCt Low].

For a given edge e, the more samples we have in We,R, the better the estimation of the true underlying distribution, and thus the more accurate the confidence level.

Goel and Salganik [ 5] have suggested that RDS estimates are less accurate and confidence limit intervals wider than originally thought.