Timeline for Covariance in multivariate Gaussian
Current License: CC BY-SA 4.0
10 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 17, 2021 at 10:38 | vote | accept | Jose Ramon | ||
| Jun 17, 2021 at 10:36 | comment | added | Tim | @JoseRamon because it tells you nothing about covariance between the variables. | |
| Jun 17, 2021 at 10:33 | comment | added | Jose Ramon | that does not really answer my question. Visually in the example, I have posted, why the variance in the two dims $\sigma_{X}$ and $\sigma_{Y}$ isn't enough to be used? | |
| Jun 17, 2021 at 10:20 | comment | added | Tim | @JoseRamon it represents the covariance matrix between the variables that together form the multivariate distribution. | |
| Jun 17, 2021 at 10:18 | comment | added | Jose Ramon | I understand what is the covariance matrix, but I am not sure why it is necessary and what it represents in the multivariate case. | |
| Jun 17, 2021 at 10:16 | comment | added | Tim | @JoseRamon to have a distribution for variables that are Gaussian and correlated. If you don't need that, you don't need the distribution. | |
| Jun 17, 2021 at 10:14 | comment | added | Jose Ramon | yes, I am trying to grasp the reason that why make use of it in our case. | |
| Jun 17, 2021 at 10:12 | comment | added | Tim | @JoseRamon are you familiar with correlation? | |
| Jun 17, 2021 at 10:05 | comment | added | Jose Ramon | IIs there a way to explain this visually? How the correlation affects the Gaussian? | |
| Jun 17, 2021 at 9:59 | history | answered | Tim | CC BY-SA 4.0 |