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BC is usually computed for street segments.
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Considering the high number of molecular descriptors that are usually computed for a dataset, which could be in the range of thousands, a common practice consists of exploring different feature selection methods in order to obtain different smaller subsets of relevant descriptors [1, 5].
The maximum likelihood estimation (MLE) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat{\thetaa} $$\end{document} θ ^ for l O is usually computed using the Newton method for efficiency.
In comparison, for many systems that monitor LeqT, pRMS is usually computed over 1-s intervals [16].
The MFCC and PLP features are usually computed from a Hamming-windowed periodogram spectrum estimate.
"Phylogenetic trees" are widely used to address this task and are usually computed from molecular sequences.
Although accuracy figures for different parsers are commonly reported in the NLP literature, such figures are usually computed using artificial metrics that, while useful for parser development, may not be indicative of overall task performance when the parser is used as a component in a biomedical text mining system.
p-values for paired binary data are usually computed using a risk difference based (McNemar) test.
These curvatures are usually computed using finite differences.
These indices are usually computed by direct Monte Carlo or quasi-Monte Carlo simulation, which may reveal hardly applicable for computationally demanding industrial models.
For a fixed code, permutation patterns are known a priori and the indices/addresses that indicate which nodes connect with each other are usually computed offline.
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