Finally, we transformed the approximate 95% CI for ln CE) (its point estimate plus or minus 2 sd) to the 95% CI for CE by exponentiating the upper and lower bounds of the confidence interval for ln CE), resulting in the error bars on the plot.
This helps transform the RBDO problem into an approximate deterministic optimization problem which can be solved by standard optimization algorithms.
Because often this assumption is not fulfilled, researchers normally approach phenotypes in three ways: (a) they ignore the lack of normality in the phenotypes; (b) they transform the non-normal phenotype to approximate it to normality; or (c) they use generalized linear mixed models (GLMMs) to model the appropriate distribution of the phenotype (Stroup 2015).
This equation is further transformed to the approximate solution as shown by equation 4. Similarly, from R1-R6 an equation representing the dynamics of phosphrylated αCaMKII can be constructed.
A pragmatic approach here is to transform such variables to approximate normality before imputation and then transform the imputed values back to the original scale.
To transform the source locations coordinates into Talairach coordinates, the most anterior and posterior points of the sphere, the most left and right points of the sphere, as well as the most superior and inferior points of the sphere were coregistered with the normalised brain, thereby transforming the sphere into an ellipsoid approximating the shape of human brain.
In case of integer to integer wavelet transform that approximates LeGalle 5/3 filter, and are shown in the following.
Once directed Hough transform has determined the approximate location of an iliac crest, the ASM starting shape is initialized using a specific reference point on the Hough transform circle, as well as knowledge of the pelvic ring position.
The complex wavelet transform provides approximate shift invariance and good directional selectivity, and attempts to solve the weakness of real discrete wavelet methods.
This introduces limited redundancy (2m:1 for m-dimensional signals) and allows the transform to provide approximate shift invariance and directionally selective filters (properties lacking in the traditional wavelet transform) while preserving the usual properties of perfect reconstruction and computational efficiency with good well-balanced frequency responses.
