Timeline for ergodic theory for markov processes
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
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| May 20, 2016 at 19:09 | comment | added | jkt | I am also interested in the second order derivative wrt the parameter $\theta$, i.e. $\frac{1}{T}\sum_t \partial_i \partial_j \log p(x_t \mid x_{t-1},\theta)$. This is related to the question here which I asked with a bounty of 50, though no one answered. I expect this would be related to the Fisher matrix in the standard iid data case. I am mainly interested in the generalization of the classical statistical setting to the Markovian setting. | |
| May 20, 2016 at 18:42 | comment | added | jkt | Thanks, I actually figured out the expectation wrt $\pi(x_t,x_{t-1})$ on my own later on but I did not express the whole thing in terms of entropy terms. | |
| May 20, 2016 at 18:39 | vote | accept | jkt | ||
| May 20, 2016 at 15:24 | history | edited | Yair Daon | CC BY-SA 3.0 |
added some explanation about conditional entropy and the trick to use
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| May 20, 2016 at 14:00 | history | answered | Yair Daon | CC BY-SA 3.0 |