Timeline for Find probability of non-stationary inputs into Turing machine?
Current License: CC BY-SA 4.0
22 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| S Apr 8, 2019 at 19:27 | history | suggested | Artemy | CC BY-SA 4.0 |
clarify that $x$ is drawn from a non-stationary process
|
| S Apr 8, 2019 at 19:25 | history | bounty ended | litmus | ||
| S Apr 8, 2019 at 19:25 | history | notice removed | litmus | ||
| Apr 8, 2019 at 19:25 | vote | accept | litmus | ||
| Apr 8, 2019 at 17:36 | answer | added | Artemy | timeline score: 1 | |
| Apr 8, 2019 at 17:34 | review | Suggested edits | |||
| S Apr 8, 2019 at 19:27 | |||||
| Apr 8, 2019 at 17:29 | comment | added | Artemy | @litmus Sure, that sounds good. | |
| Apr 8, 2019 at 14:49 | comment | added | litmus | @Artemy Yes, that is exactly what I wanted to know, thanks! If you want to elaborate your comment as an answer instead, I'd be very happy to accept it. Also, please feel free to edit my question if you think that there are some parts that could be more clear for posterity. | |
| Apr 7, 2019 at 0:06 | comment | added | Artemy | That helps but I might still not be entirely understanding your question. The equation you have states exactly how to compute (or, more specifically, lower semicompute) $P_M(x)$ for any finite string $x$. Are you asking whether it is allowed for $x$ to be sampled from some non-stationary random process, say with distribution $Q$? If so, then yes -- the above probability measure is defined for any finite string whatsoever. | |
| Apr 6, 2019 at 20:19 | comment | added | litmus | @Artemy Hello again Artemy! Thank you for the comments, I have updated the question. | |
| Apr 6, 2019 at 20:16 | history | edited | litmus | CC BY-SA 4.0 |
deleted 43 characters in body
|
| Apr 6, 2019 at 20:10 | history | edited | litmus | CC BY-SA 4.0 |
deleted 43 characters in body
|
| Apr 6, 2019 at 19:56 | comment | added | Artemy | @litmus I think your question shows some confusions. First, I believe you are using $s_i(x)$ to refer to the $i$-th program which generates $x$, which should be explained. Second, it is not $s_i(x)$ that has a universal distribution, rather the "universal distribution" is $P_M$ itself, as defined above. Lastly, it is not clear what you mean by a "non-stationary binary string". Usually a random process (i.e., a distribution over infinite strings) is said to be is stationary or not stationary, not a particular finite string. | |
| Apr 5, 2019 at 12:52 | comment | added | usul | Interesting, but I'm a bit confused. I mean, I can use any probability to predict anything. So are you asking whether it's a good idea or not? That depends where $x$ comes from. But this feels philosophical ... also, it's not computable, so in that sense, I think "no, not possible". | |
| Apr 5, 2019 at 10:35 | history | edited | litmus | CC BY-SA 4.0 |
deleted 9 characters in body
|
| S Apr 5, 2019 at 10:29 | history | bounty started | litmus | ||
| S Apr 5, 2019 at 10:29 | history | notice added | litmus | Draw attention | |
| Apr 5, 2019 at 10:28 | history | edited | litmus | CC BY-SA 4.0 |
added 174 characters in body
|
| Apr 3, 2019 at 7:20 | review | First posts | |||
| Apr 3, 2019 at 7:36 | |||||
| S Apr 3, 2019 at 6:39 | history | suggested | Rodrigo de Azevedo | CC BY-SA 4.0 |
I would blame auto-complete for the typo.
|
| Apr 3, 2019 at 6:22 | review | Suggested edits | |||
| S Apr 3, 2019 at 6:39 | |||||
| Apr 3, 2019 at 5:31 | history | asked | litmus | CC BY-SA 4.0 |