Timeline for Conditional Expectation in Diffusion Process
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Oct 29 at 10:24 | comment | added | Nate River | Ah great, i have undeleted my answer then. | |
| Oct 29 at 9:35 | comment | added | Mingzhou Liu | Indeed, I am conditioning on $\sigma(X^i)$. However, I would be interested to hear your thoughts on conditioning on $\sigma(W^i)$. | |
| Oct 29 at 4:30 | comment | added | Nate River | I notice that you conditioned on $\sigma(X^i)$, instead of $\sigma(W^i)$, rendering my approach invalid… | |
| Oct 29 at 4:10 | comment | added | Nate River | Are you okay with $\lambda$ being, say $C^2$, so that we can apply Ito’s? We can get something more then. | |
| Oct 29 at 4:05 | comment | added | Nate River | I’ve added the (tiny amount of) progress i’ve been able to make. Nice problem! | |
| Oct 29 at 4:05 | answer | added | Nate River | timeline score: 1 | |
| Oct 29 at 3:54 | comment | added | Mingzhou Liu | What I want to estimate is the function $t\mapsto \mathbb{E}[X_t^i|\mathcal{F}_t^C]$ (like the objective in optimal filtering). What do you mean by "freeze the Brownian motions that are being conditioned on"? | |
| Oct 29 at 3:37 | comment | added | Nate River | What kind of estimate do you want? We can freeze the Brownian motions that are being conditioned on to obtain something. | |
| Oct 29 at 3:08 | history | asked | Mingzhou Liu | CC BY-SA 4.0 |