Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with covariance
Search options not deleted
user 380062
Covariance is a quantity used to measure the strength and direction of the linear relationship between two variables. The covariance is unscaled, & thus often difficult to interpret; when scaled by the variables' SDs, it becomes Pearson's correlation coefficient.
2
votes
0
answers
112
views
Covariance of a vectorized random matrix
In my case, the row covariance matrix is $\mathbf{I}_{n\times n}$ due to the row independence, however, the column covariance instead of being a $n\times n$ matrix is an $n^2\times n^2$ block diagonal. … My question is what is the variance-covariance matrix of the random matrix $\mathbf{A}$ if the covariance of $vec(\mathbf{A}^\intercal)$ is $\boldsymbol{\Sigma}=\text{bdiag}(\boldsymbol{\Sigma}^1,\dots …
- 23