Timeline for How to find the conditional distribution of gaussian from covariance matrix?
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 11, 2018 at 18:59 | vote | accept | ironman | ||
| Jun 6, 2018 at 14:29 | comment | added | byouness | You are welcome, always glad to help when I can. Maybe it's simply a typo and what they wanted to say was inverse of Cholesky decomposition. | |
| Jun 6, 2018 at 11:38 | comment | added | ironman | It is given in some undirected graphical model slides in google. Unable to find now. This bloody inverse covariance matrix is creating problem to me. I don't understand its purpose here. But thanks sir anyway. | |
| Jun 6, 2018 at 11:26 | comment | added | byouness | Completed my answer to clarify all this. I hope it's clear now. Would be good if you include the reference of the citation you used in the question. Thanks! The fact that the inverse of Cholesky's decomposition of a matrix $C$ is fully characterized by the inverse of the matrix $C^{-1}$ doesn't look straightforward to express explicitly unfortunately. | |
| Jun 6, 2018 at 11:22 | history | edited | byouness | CC BY-SA 4.0 |
completed answer
|
| Jun 6, 2018 at 11:03 | history | edited | byouness | CC BY-SA 4.0 |
added 9 characters in body
|
| Jun 6, 2018 at 10:48 | comment | added | ironman | Thanks Sir for answering. But I have one more doubt. I understand that using covariance matrix we can write the conditional value. But what is the need of inverse covariance matrix here. I will be obliged if you explain your answer a little bit more. Would you explain me with a small example! | |
| Jun 6, 2018 at 10:37 | history | answered | byouness | CC BY-SA 4.0 |