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Let L be a matrix square root of covariance matrix, i.e., L L T =P.
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where d is the dimension of the augmented state, ξ UKF is an algorithm parameter, and (sqrt {P}) is a matrix square root for which: sqrt{P}sqrt{P}^{T}=P. (16).
For m = q and Ψ = 0, Γ is a matrix square root of Σ.
Matrix rank computation was quick because the matrices evaluated were small the largest matrix would be a square matrix with the dimension of DoF-1 and DoF-1 andely invariant.
In this case, the ({mathbf{G}}) matrix will be a square matrix which could be invertible if the determinant of the matrix is different from zero.
A Latin square is a matrix containing the same number of rows and columns.
The covariance/correlation matrix of the reference channels will then be a square matrix of the appropriate dimension in each case.
A square matrix is a matrix with the same number of rows and columns.
We propose to extend the n-mode product to multiply a tensor with a matrix along several dimensions, combined in A l. Let D ∈ C I A l × I A l be a square matrix.
Let P be a square matrix.
Let Q be a square matrix.
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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