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We also point out that the authors in[3, 4] and[2] did not investigate how their methods would perform on very short audio segments.
Although the general consensus of Klein et al. [24] suggested that image registration methods would perform better on properly skull-stripped images than on whole-head images, no study has yet been published that made this comparison.
Although the theoretical justifications of these methods for fitting any statistical distributions to data can be found in the literature (Aldrich 1997, Fisher 1922, Hosking 1990), it is unclear how these methods would perform in the case of GLDs.
Although the obtained results demonstrated a consistent performance improvement of FDE schemes over the methods without FDE functionality, the question remains on how the FDE methods would perform in more advanced hybrid (sensor fusion) solutions (Scenario 2) and dynamic scenarios (the vessel is moving).
Adjustment for multiple testing can be considered and applied, although it is unclear how such methods would perform in actual practice [36].
We varied the values of k (k = 30, 20, 10, 1) to simulate how the five statistical methods would perform in these different artificial cases.
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One way to mitigate this issue is to establish a reliable performance model that allows a numerical analyst to make reliable predictions of how well a numerical method would perform on a given computer architecture, before embarking upon potentially long and expensive implementation and optimization phases.
Further work could be done to investigate how well the method would perform with different choices of distribution, or when applied to data generated from a different distribution.
Based in this observation, we considered it unlikely that another peak finding method would perform substantially better on our data than ChIP-peak with optimized parameter settings.
SNPs from the pooling data were prioritised for individual genotyping based on three different methods, outlined below, since, at the time of this experiment we did not know which method would perform best.
From the features that we generated for 1,200 mRNAs for humans and 4,000 mRNAs for mouse, we wanted to check how our method would perform on mRNAs that it had not seen before.
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