For instance, VanRaden [ 2] described an iterative method to implement approximations of both BayesA and BayesB.
The analytic wavelet transform (AWT) has been implemented on approximation signal followed by a time marginal integration (TMI) of the AWT scalogram.
We implemented our approximation within IMPUTE2, which uses an iterative algorithm to impute untyped variants in GWAS datasets.
We also provide a method for implementing an approximation of the regularized inversion of a SV-matrix, under the assumption of having smoothly spatially varying kernels, and enough regularization.
We also have proposed a method for implementing an approximation of the filtering of a SV-matrix regularized inverted, under the assumption of having smoothly varying kernels, and enough regularization.
In many real cases, there is a substantial amount of overlapping among the kernels with different deformations, so one can use a J ≪ R. Now we try to use the technique of deformable filtering to efficiently implement an approximation of the transposed SV-filtering, which is made by using the local PSFs as local IKs (see Section 2.1).
This shows that even with implementing linear approximation and using severely limited data sets, the underlying structures can be recovered with accuracy.
A local adaptation of the sensitivity of the photoreceptors to an average illumination level can also be seen as implementing an approximation of a logarithmic transformation, provided that both the baseline and the sensitivity regarding deviations from the baseline are adapted in a corresponding manner.
The MLS method is implemented for the approximation of the shape functions used as basis functions.
An implemented computationally tractable approximation to the ideal planner is discussed and its behavior is described.
