Sentence examples for good lower bound from inspiring English sources

Finally, we show that theoretically predicted sample sizes using the so-called S-estimates provide a good lower bound.

This approximation may be considered as a good lower bound for any MIMO system in the presence of impulsive noise.

It may be considered as a good lower bound for 2×2 MIMO systems in the presence of impulsive noise.

However, the Cartesian P ∗ -property of SCLCP still allows us to find a good lower bound of the inner product 〈 d x, d s 〉.

A good lower bound can be formulated by ensuring that the negative of the absolute value of the scaled samples at each sampling point is greater than the negative of the absolute value of the scaled peak.

Obtaining a "good" lower bound is somewhat more involved.

The method, which can easily be generalized to other geometries, is relatively simple to use and converges rapidly to a very good upper and lower bound.

Adler et al. showed that if the hunter chooses a good strategy, the upper bound of the expected time that the hunter catches the rabbit is O(n log(d i a m(G))), where d i a m(G) is a diameter of a graph G, and if the rabbit chooses a good strategy, the lower bound of the expected time that the hunter catches the rabbit is Ω(n log(d i a m(G))) [1].

In our study, the AUC was not good, as the lower bound of the 95% confidence interval was below 0.75 [ 27].

Taking into account the good quality of the lower bound (see Example 5) and the observation that most of the time is needed to prove optimality, we may assume that the "true" gap is even smaller.

Hence, on the average, a good estimate for a lower bound on the number N of overall samples is equal to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\frac {1.000}{\alpha }$\end{document} 1.000 α.