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Exact(2)
In the RAS calculation, a matrix for approximation is required.
A general formulation for the Chebyshev polynomial-based weighting coefficient matrix for approximation of fractional derivatives has been derived.
Similar(57)
At convergence of the iterative reweighed least squares algorithm, the negative Hessian represents the inverse covariance matrix for the Gaussian approximation to the posterior distribution [26].
The corresponding analytical expressions are formulated considering ABCD matrix for hyperbolic microlens following paraxial approximation and also Gaussian field distributions are considered for both the source and the fiber.
This provides substantially faster computation for multiple phenotypes but the use of the same error covariance matrix for all phenotypes is an approximation.
SURF uses a very basic Hessian matrix approximation for feature point detection.
In this paper, we propose to estimate non-noisy low-rank matrix or denoised approximation for the low-rank matrix in (5) that will provide an overall improved performance of fMRI signal reconstruction using low-rank plus sparse decomposition.
The major difference between the computation of the global stiffness matrix for SIPG and conventional continuous Galerkin approximations is the requirement to evaluate inter-element face terms, this significantly increases the computational effort.
In this paper, we study a perturbation theory for low-rank matrix approximation.
A best matrix approximation technique for updating the analytical model is developed using the known modal parameters.
For instance, our matrix approximation procedure was much more efficient with the original double-mutant fitness measurements, as provided by the SGA and GIM laboratories, compared to the highly processed and scored E-MAP datasets.
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