Skip to main content
edited tags
Link
Carlo Beenakker
  • 188.1k
  • 18
  • 448
  • 651
edited tags
Link
Carlo Beenakker
  • 188.1k
  • 18
  • 448
  • 651
edited body
Source Link

This question comes from https://stats.stackexchange.com/questions/457375/recover-full-covariance-matrix-from-covariance-diagonal-and-precision-off-diagon where it have not found answers. So, let $\Sigma$ be an $N\times n$$N\times N$ covariance (that is, positive semidefinite) matrix, but we do only know its diagonal and the off-diagonal elements of its inverse $\Sigma^{-1}$ (known as precision matrix).

How can we find $\Sigma$ effectively?

This question comes from https://stats.stackexchange.com/questions/457375/recover-full-covariance-matrix-from-covariance-diagonal-and-precision-off-diagon where it have not found answers. So, let $\Sigma$ be an $N\times n$ covariance (that is, positive semidefinite) matrix, but we do only know its diagonal and the off-diagonal elements of its inverse $\Sigma^{-1}$ (known as precision matrix).

How can we find $\Sigma$ effectively?

This question comes from https://stats.stackexchange.com/questions/457375/recover-full-covariance-matrix-from-covariance-diagonal-and-precision-off-diagon where it have not found answers. So, let $\Sigma$ be an $N\times N$ covariance (that is, positive semidefinite) matrix, but we do only know its diagonal and the off-diagonal elements of its inverse $\Sigma^{-1}$ (known as precision matrix).

How can we find $\Sigma$ effectively?

Source Link
Loading