To be able to estimate MAI in each iteration, we first calculate the estimation of matrix Y ‾ ( k ) using the extrinsic information of the previous iteration.
The left hand side of the equations (matrix Y) was computed using discretization and the experimental data (Eq. 3).
By applying a method widely used in I O analysis, it is possible to make two types of square matrices, Y and Y*, using the E and R tables.
By applying the method widely used in input output analysis, it is possible to make two types of square matrices Y and ( Y^ ), using tables E and R. Table Y is based on a fund-employment portfolio, while ( Y^ ) is founded on a fund-raising portfolio.
t=score vector for X; A=the number of PLS components; p=loading vector for X; c=loading vector for Y; E=residual matrix for X; F=residual matrix for Y; When using PLS for discriminant analysis (PLS-DA), dummy variables are used to describe the classes to which different samples can be assigned.
For those reactions, the model has been reduced as described in Equation (15) using a data matrix Y completed by the means μ j, k of the a priori distributions.
To solve for the potential field it is convenient to use the admittance matrix Y R of the resistance network.
In this subsection, we describe the structure of financial markets using the financial transaction matrix (Y table) and power of dispersion indices calculated from the Leontief inverse matrix.
We note that o and om are obtained using the relevant rows of the matrix Y × X.
