Your English writing platform
Discover LudwigExact(8)
A method to estimate dispersion relations and warping associated with elastic wave propagation in a bar is presented.
Multichannel analysis of surface waves (MASW; Park et al. 1998, 1999) is currently the most effective method to estimate dispersion curves from multichannel seismic data.
For this reason, a second way to estimate dispersion is to compute the standard deviation for the longitudes and latitude separately and use them to build a bounding box around the centroid of the Cartesian coordinates (Figure 2(b)).
The edgeR functions estimateCommonDisp and estimateTagwiseDisp were used to estimate dispersion.
The standard deviation was calculated to estimate dispersion of this parameter.
Although edgeR and DESeq use different algorithms to estimate dispersion, the number and identity of differentially expressed genes were the same for DESeq- or TMM-normalized data (Fig. 4), indicating that either software package could be used for these estimates.
Similar(52)
DESeq [ 5] uses a similar negative binomial model as edgeR but models the observed relationship between the mean and variance when estimating dispersion, allowing a more general, data-driven parameter estimation.
The major conclusion of this study is that estimating dispersion within the urban canopy requires flow information below the canopy top.
Meanwhile, the CMP model with estimated dispersion parameter, (hat {nu }=0), again suggests to consider a geometric model with success probability (1-hat {lambda } = 0.466).
The CMP (i.e. the sCMP(m=1)) model does reasonably well, as evidenced by the resulting log-likelihood and AIC values ( −260.3649 and 524.7298, respectively); the CMP estimated dispersion parameter, (hat {nu } = 0.3150), indicates recognized over-dispersion in the dataset.
A comparison of the estimated dispersion curve and the theoretical dispersion curve calculated from the present velocity structure model would help to identify areas in which further improvement in the model is necessary.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com