Sentence examples for matrix we need from inspiring English sources

As in Section 4.1, to obtain the ML estimate of a persymmetric covariance matrix, we need the forward-backward (FB) log likelihood, which is the combination of the forward-looking and the backward-looking log likelihoods.

In order to measure the degree of mobility implicit in a given transition matrix we need to find a measure of the "distance" of this matrix with respect to the identity matrix, which represents absolute immobility across quantiles.

To obtain the stochastic matrix, we need to have sums elements of every row of the matrix to sums up to one, but as not all the nodes have out degree edges its inevitable to have rows of all zero which does not sum up to one.

To generate the transition probability matrix, we need to estimate κR and κY.

In order to establish and simplify various matrix equalities composed of generalized inverses of matrices, we need the following well-known rank formulas for matrices to make the paper self-contained.

In order to solve this problem for the case of normal skew J-Hamiltonian matrices, we need to obtain the normal skew J-Hamiltonian solution of the linear matrix equation AY=YLambda.

Therefore, in order to use those matrices, we need to change the sign of each entry, i.e., take its dual.

As the covariance matrix is a block Toeplitz and Hermitian, due to these properties of covariance matrix, we only need to evaluate its first block.

For the covariance matrix, we first need to know the steering vectors and powers of all interferences, and the noise power.

Here I is the identity matrix with dimension as of covariance matrix R. γ 1,γ 2,γ 3....γ k are the characteristic vectors of R. To form a feature matrix, first, we need to select G, that is, the number of most significant eigenvector corresponds to the highest eigenvalues where 1≤G≤k.

To obtain the values of each one of the components of the matrix A, we need to solve the EEG forward problem [38]: Given the electrical activity of the current sources within the brain and a model for the geometry of the conducting media (brain, skull and scalp, with its corresponding electric properties), compute the resulting EEG signals.