Timeline for Equivalent method for maximum likelihood estimation of covariance parameters
Current License: CC BY-SA 3.0
9 events
| when toggle format | what | by | license | comment | |
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| Dec 13, 2013 at 17:51 | comment | added | Michael Hardy | I'm not sure it matters, but I wondered to what extent this resembles things I've seen before. | |
| Dec 13, 2013 at 14:04 | comment | added | Ruben van Bergen | Well, strictly speaking I have a matrix $B$ in $\mathbb{R}^{m \times n}$ (where $m$ is indeed the sample size) which is my actual dataset. $W$ is a parameter that I estimate independently, and $X = B - WC$, where $C$ is an $m \times k$ matrix which forms a basis as a function of a variable $s$ (which in this (training) stage of the analysis is known, but later I will try to estimate given some test data and the parameter estimates). I was worried that all of that background might only make things more confusing so I left it out - do you think it matters for my question? | |
| Dec 13, 2013 at 1:36 | comment | added | Michael Hardy | You wrote "$X$ and $W$ are known matrices". The expression $L(\bullet\mid W,X)$ makes me think they're your data, i.e. realizations of random vectors, where you know a parametrized family of probability distributions, from one of which they were drawn. Is $m$ your sample size? For $X$, at least? | |
| Dec 13, 2013 at 1:20 | comment | added | Michael Hardy | I'll probably change my mind about this a few minutes after starting to think about it, but I might begin by asking whether the methods on this page could be adapted to the present situation: en.wikipedia.org/wiki/… | |
| Dec 13, 2013 at 1:12 | history | edited | Michael Hardy | CC BY-SA 3.0 |
added 21 characters in body
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| Dec 10, 2013 at 14:27 | answer | added | lmg | timeline score: 1 | |
| Dec 10, 2013 at 14:14 | history | edited | Ruben van Bergen | CC BY-SA 3.0 |
edited body
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| Dec 10, 2013 at 13:19 | review | First posts | |||
| Dec 10, 2013 at 13:19 | |||||
| Dec 10, 2013 at 12:59 | history | asked | Ruben van Bergen | CC BY-SA 3.0 |