Timeline for Reference on continuous-time finite state filtering
Current License: CC BY-SA 4.0
11 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 22, 2021 at 4:09 | history | edited | YCor | CC BY-SA 4.0 |
added tag, removed capitals (the question was bumped anyway)
|
| S Jun 22, 2021 at 3:30 | history | suggested | ABIM | CC BY-SA 4.0 |
Made title a bit more clear and to the point :)
|
| Jun 21, 2021 at 19:09 | review | Suggested edits | |||
| S Jun 22, 2021 at 3:30 | |||||
| Jun 21, 2021 at 19:06 | answer | added | ABIM | timeline score: 2 | |
| Mar 16, 2012 at 8:08 | vote | accept | Tomas | ||
| Mar 15, 2012 at 20:33 | comment | added | Jason Swanson | The formula $P(X_t=k\mid Y_t=i)=\pi_k(i)$ can be reformulated as $P(X_t=k\mid Y_t)=\pi_k(Y_t)$, and this formula makes perfectly good sense, no matter what the distribution of $Y_t$. See en.wikipedia.org/wiki/Conditional_expectation#Formal_definition and en.wikipedia.org/wiki/…. | |
| Mar 15, 2012 at 14:05 | answer | added | Joris Bierkens | timeline score: 3 | |
| Feb 29, 2012 at 9:44 | comment | added | Tomas | @ Stéphane Laurent Could recommend some literature (papers) on this the conditional stochastic processes topic. Because all I can find is just short notes in textbooks and no deeper analysis. I would be greatfull for it. | |
| Feb 28, 2012 at 15:36 | comment | added | Stéphane Laurent | It is easy to extend: for each $t$ there is a conditional law of $X_t$ given $Y_t$. | |
| Feb 28, 2012 at 13:02 | history | edited | Tomas | CC BY-SA 3.0 |
added 5 characters in body
|
| Feb 27, 2012 at 17:34 | history | asked | Tomas | CC BY-SA 3.0 |