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This method gives uncertain results, especially in recent years, because there remains confounding by potential changes in the interval between infection and death and because numbers of cases and deaths are informative of incidence rates sometime in the past rather than current incidence.
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The method gives rise to frustration.
Illustrative examples show that for the given uncertain quantum system, the method presented in this paper has improved performance over the existing quantum H∞ control results without considering uncertainties.
However, these methods are rather time-consuming, labor-intensive and may give uncertain results due to a lack of mastitis-associated species in database or misinterpretation [ 4].
Probabilistic methods have been applied in computational models to predict distributions of performance given uncertain or variable parameters.
This is achieved by deducing an online Bayes optimal estimate of the true measurand given uncertain, noisy or incomplete measurements within the framework of sequential Monte Carlo methods.
In this paper non-probabilistic convex models of uncertainty are used to formulate reliability in terms of acceptable system performance given uncertain operating environment or uncertain geometrical imperfections.
Furthermore, as expected, for unclear situations, the classifier gave uncertain classifications.
Again, if the sample size was < 100, a score will be given (uncertain evidence).
Specifically, a easily testable mode- and delay-dependent controller design method is given for uncertain linear systems.
Both methods gave comparable results.
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CEO of Professional Science Editing for Scientists @ prosciediting.com