Timeline for Simple random walk with an extra condition
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
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| Jul 12, 2022 at 7:21 | vote | accept | Sia-TeX | ||
| Jul 11, 2022 at 0:34 | comment | added | Yuval Peres | As I mentioned, the exponential decay of covariances follows, e.g. from pages.uoregon.edu/dlevin/MARKOV/mcmt2e.pdf Theorem 4.9. The sequence $Y_n$ is a Markov chain when $n$ runs through all positive integers. Read about shifts of finite type and the Parry measure in the references [4] [5] above. | |
| Jul 10, 2022 at 19:10 | comment | added | Sia-TeX | Thank you very much for the answer and the sources. In general I am interested, in what happens when we inject some predictability (by imposing some rules) into a random setting. Also assuming there are certain rules, how long it would take for a machin to learn these hidden rules. I have two questions about the elementary approach: 1- How from irreducible +aperiodicity of $Y_n$, we can conclude that $X_m$ and $X_n,$ with $m \neq n$, are nearly independent. 2- For $Y_n$ we should have n=0, 5, 10, ... for it to be a MC, correct? | |
| Jul 10, 2022 at 3:36 | history | edited | Yuval Peres | CC BY-SA 4.0 |
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| Jul 10, 2022 at 3:08 | history | edited | Yuval Peres | CC BY-SA 4.0 |
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| Jul 10, 2022 at 3:01 | history | answered | Yuval Peres | CC BY-SA 4.0 |