Timeline for uniquely determining a distribution using moments
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jan 21, 2018 at 1:20 | comment | added | Soheil Feizi | yes, that'd be helpful too. | |
| Jan 19, 2018 at 23:51 | comment | added | S.Surace | Are you also interested in knowing how many statistics you need to know (not necessarily moments)? This would probably be a simpler question. | |
| Jan 18, 2018 at 17:30 | comment | added | Soheil Feizi | Thanks guys. Just to clarify, the goal is not to uniquely determine the $\phi$ functions, but to uniquely determine the distribution. I.e., if $m_k(P_1)=m_k(P_2)$ for 1\leq k\leq K, we can conclude that $P_1=P_2$. | |
| Jan 18, 2018 at 8:29 | answer | added | Bjørn Kjos-Hanssen | timeline score: 5 | |
| Jan 18, 2018 at 8:00 | comment | added | Bjørn Kjos-Hanssen | @michael Even in the 1-dimensional case: there you cannot distinguish $-x$ from $x$ | |
| Jan 18, 2018 at 7:51 | comment | added | user83457 | in the multidimensional case many polynomials will give the same distribution so you cannot determine the polynomial from moments, e.g., $(x_1x_2, x_1^2)$ and $(x_1x_2, x_2^2)$ | |
| Jan 18, 2018 at 0:52 | history | edited | Soheil Feizi | CC BY-SA 3.0 |
added 10 characters in body
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| Jan 17, 2018 at 23:55 | history | asked | Soheil Feizi | CC BY-SA 3.0 |