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There are several threads here about covariance matrix and inverse covariance matrix interpretation (here, here or here).

However, I was wondering how to interpret the inverse covariance matrix (or precision matrix) as the covariance matrix can be diagonalized (since it's a definite positive matrix). $$ \Sigma = P D P^{-1} $$ ($\Sigma$ being the covariance matrix and $D$ is a diagonal matrix). Then $$ \Sigma^{-1} = P D^{-1} P^{-1} $$

How to interpret this diagonal precision matrix $D^{-1}$ ? I think I don't get exactly the signification of the eigenvectors $P$ .

Thanks for your help.

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  • $\begingroup$ The eigenvectors are the same in both cases--$P$ and $P^{-1}$ play their roles in both formulas--and therefore are subject to exactly the same interpretations. Which part don't you "get exactly"? $\endgroup$
    – whuber ♦
    Commented Jun 8, 2015 at 21:53
  • $\begingroup$ What I don't get is the signification of a covariance matrix when this matrix is diagonal. And also the signification of the inverse covariance matrix when this matrix is diagonal. $\endgroup$
    – max
    Commented Jun 9, 2015 at 16:46
  • $\begingroup$ I think your last comment clarified what you were asking; this was not very clear in your original question. Please see my answer below. $\endgroup$
    – usεr11852
    Commented Oct 26, 2015 at 7:42

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