Sentence examples for much more noisy from inspiring English sources
The bottom plot shows a much more noisy aspect than the upper plot.
As expected the dynamic wave power spectra of the electric field data for the four spacecraft (Fig. 7) are much more noisy than the corresponding spectra of the magnetic field (Fig. 6).
"Fixation-introduced variants tend to be randomly distributed all over the place, so if you sequence very deep, then you should be able to see the true variants and exclude the noise," says Hedegaard, adding that "in general the data are much more noisy when the DNA is of FFPE origin".
"My previous work was much more noisy, but I simply couldn't bring myself to add superfluous elements and ambiguity to hide behind in these new paintings.
As shown in Figs. 5(c) and 5(d), both the relative and local optical axes calculated under the assumption of zero diattenuation were much more noisy and irregular than those obtained after removing diattenuation (Fig. 1).
Since Blimp-1 is expressed at a low level in the B cell [ 66], it exhibits a pulsatile expression pattern and is much more "noisy" than Bcl-6 or Pax5.
What probably gets under my colleagues' skin much more is how noisy I am: the muttered curses when my computer gives me the blue screen of death or the impromptu phone calls with authors that end up lasting an hour (sorry guys!).
That means our approach performs much more stably under noisy circumstances.
I found myself wondering what this visionary roamer might have thought of his beloved America now, so much more crowded, sprawling, noisy, full of electronic news and nonsense and political insanity.
However, this minimum is much more difficult to estimate in noisy data, and microarray data is inherently noisy.
A comparison of Figure 1 parts (A) and (B) shows that a functional association prior improves inferences for GNW-generated data (from a noisy, nonlinear model) much more than for GSP-generated data (from a less noisy, linear model) if the actual sparsity level is unknown; this is shown by the difference in mean (dark blue) or median (light blue) boxes at the same prior accuracy.
