Timeline for Moment problem for a discrete probability distribution
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| Jul 30, 2023 at 5:31 | vote | accept | Yaroslav Bulatov | ||
| Mar 20, 2023 at 17:03 | answer | added | Daniel Lichtblau | timeline score: 4 | |
| Mar 20, 2023 at 17:03 | comment | added | Yaroslav Bulatov | I may be using the wrong terminology. $m(k)$ gives $E_X[X^0 p(X)^k]$ rather than $E_X[X^k]$ (is there a name for it?). It seems there's a formula to invert the mapping from $m(1),\ldots,m(d)$ although it's not likely to work for the example above | |
| Mar 20, 2023 at 16:39 | comment | added | Daniel Lichtblau |
Also I think your m[t] is not correct for defining the tth moment. I think you want the sum of i^t*h[i] for i in the range of the distribution.
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| Mar 20, 2023 at 9:35 | comment | added | Daniel Huber | Your function "invert" does not use its argument. | |
| Mar 20, 2023 at 1:56 | history | edited | Yaroslav Bulatov | CC BY-SA 4.0 |
edited title
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| Mar 20, 2023 at 1:51 | history | asked | Yaroslav Bulatov | CC BY-SA 4.0 |