The covaraince matrix is a crucial input in our analysis. In our analysis, we estimated the covaraiance matric from several off-source samples.
We use the invesre of the covaraince matrix to compute the weigted inner product between two vectors, such as inner product between two templates or between template and on-source Y(\alpha). So, the accurate computatation of the inverse matrix is essential for getting the robust results. One can check this accuracy using the following property, the inverse of a matrix is such that if it is multiplied by the original matrix, it results in identity matrix.
The covariance matrix ( $`\Sigma`$ ) is a crucial input in our analysis. In our analysis, we estimated the covariance matric from several off-source samples.
We use the inverse of the covariance matrix to compute the weighted inner product between two vectors, such as inner product between two templates or between the template $`S(\alpha)`$ and on-source $`Y(\alpha)`$. So, the accurate computation of the inverse matrix is essential for getting robust results. One can check this accuracy using the following property; the inverse of a matrix is such that if it is multiplied by the original matrix, it results in the identity matrix.
