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Exact(4)

The respective vector w t+1 for the next period (t + 1) is calculated by multiplying the vector with the matrix of transition probabilities A [41], i.e. w ¯ t + 1 = w ¯ t A, with w ¯ t = w 1 … w n ; A = a 11 a 12 … a 1 n a 21 a 22 … a 2 n ⋮ ⋮ ⋮ ⋮ a n 1 a n 2 … a nn.

By multiplying the vector signal r k by the conjugate of the phase estimate of the i th path, exp(−j ψ i,k ) for i=1 and 2, the signal associated with the i th path is concentrated around the DC component.

This last step was accomplished by multiplying the vector representing the classifiers output with the ECOC decoding matrix M of KxL with entries mi,j ∈ {−1, 0, 1} where L is the number of binary classifiers and K is the number of classes (i.e., 8 target directions).

The covariance matrix of the dependent variables is found by multiplying the vector of regression weights by its transpose, and adding the residual variances and covariances of the dependent variables.

Similar(56)

Pre-multiplying the vector Y by (tilde {boldsymbol {G}}) gives a vector containing, for each individual, the average outcome of his neighbours, and similarly (tilde {boldsymbol {G}}boldsymbol {X}) is a vector of the average characteristics of his friends.

1) A linear combination score for each observation was obtained by multiplying the marker vector X with a starting coefficient vector, α (1, α ).

The population for each group of households is provided by multiplying the row vector of number of individuals in the households by the column vector of expansion factors for each household.

Multiplying the orthogonalization vector to yp we obtain, (11).

These two transforms are conducted by multiplying the feature vector by linear transform matrices.

Conformer D is aligned on conformer Q by multiplying the coordinate vector of each atom of D with M QD.

Thus by multiplying the received vector by H(H T H −1H T (projection matrix into the range space of H), we obtain an approximation of the impulsive noise.

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