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However, only the R1 random matrices represented the same combination of nucleotide preferences, though present in distinct permutations as compared to the original matrices.
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Consequently the matrices representing the differential operators (on the finest scale) have block-diagonal structure.
F d and G d are b × b matrices, representing the precoder and decoder in the virtual channel.
The covariance matrices represent the whole ensemble and the Kalman gain K e is derived from these.
W 0, W 1, and V are the interconnection matrices representing the weight coefficients of the neurons.
The spatial correlation matrices represent the second-order statistics of the channels which vary slowly and can be precisely estimated.
For example, 2-by-2 rotation matrices represent the multiplication with some complex number of absolute value 1, as above.
When n = m composition of these maps is possible, and this gives rise to the matrix ring of n×n matrices representing the endomorphism ring of Rn.
Empirical substitution matrices represent the average tendencies of substitutions over various protein families by sacrificing gene-level resolution.
Delport et al. [15], [16] pointed out that empirical substitution matrices represent the average tendencies of substitutions over various protein families by sacrificing gene-level resolution.
The weight matrices represent the frequencies of different nucleotides at each base.
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