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 90113
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
34
views
Proof for uncertainty mixing intuition
Could somewone give me a mathematical proof for this 'intuitive' result?
I have a random vector $\mathbf{Y}$ related to another random vector $\mathbf{X}$ with the equation $\mathbf{Y} = \mathbf{A}\m …
- 21
0
votes
0
answers
34
views
Second order statistics of sample statistics for random vectors
I have a set of iid random vectors $\{\boldsymbol{X}^i\}_{i=1}^N$, whose expected value is $\mathbb{E}[\boldsymbol{X}^i] = \boldsymbol{\mu}$ and whose variance - covariance matrix is $\boldsymbol{\sigma …
- 21