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7:00 PM
@AlessandroCodenotti I think you're looking for "natural irrationalities"
 
@BalarkaSen I'm not sure, we said in class that a polynomial is solvable via radicals if there is a radical extension containing its splitting field
So I'm wondering if we can't actually just use the splitting field
 
@Semiclassical Sounds like a baby version of rank correlation
 
I'll take X,Y to take values in {-1,1}
 
@AlessandroCodenotti you just don't know it a priori
 
7:01 PM
@Alessandro: Doesn't this boil down to asking whether a normal subgroup of a solvable group is necessarily solvable?
 
@TedShifrin nice correspondence
 
Where's @Mathei when he knows the answer in a millisecond?
 
then Pr(X=Y) = Pr(X=1)Pr(Y=1) + Pr(X=-1)Pr(Y=-1) and Pr(X != Y) = Pr(X=1)P(Y=-1)+Pr(X=-1)P(Y=1)
 
Natural irrationalities: Let $L$ be the splitting field of a polynomial over $K$. If $L$ contains an element $x$ that lies in some radical extension $R:K$, then there is a radical extension $R':K$ with $x \in R' \subset L$
 
@Semiclassical you should look up Spearman's rank correlation coefficient
 
7:03 PM
Take any irreducible cubic over $\Bbb Q$ with 3 real roots
 
well, for comparison
 
you need roots of unity to express the roots
 
@Semiclassical Ah, no, not Spearman. Look up Kendall's
 
@AlessandroCodenotti I think it came before Galois (@MatheinBoulomenos amirite)
 
That's exactly what you say I think
 
7:04 PM
@MatheinBoulomenos Ah, that's the casus irreducibilis business
 
ooo, yes
 
Right, we were discussing this in here a few weeks ago.
 
Interesting, thanks! @Mathei
 
oh right, it doesn't mean $L$ is radical
 
I'm going to have dinner, I'll be back soon
 
7:05 PM
Buono appetito.
 
We were only taught Spearman's rank correlation coefficient in stat but I remembered hearing a tiny bit about Kendall's
 
the only not obvious bit is the denominator in Kendall's coeff
 
@LeakyNun well, Cardano knew how to solve the cubics using roots of unity, but I don't think a proof that it is impossible to do it without roots of unity was known prior to Galois theory (I may be wrong)
 
@MatheinBoulomenos I mean, Abel proved it
 
@Semiclassical It's just the total number of possible pairings, is it not?
So as you let n --> infty you get your number by law of large numbers
 
7:06 PM
hmm
 
Abel proved that there is no general formula that solves 5th degree polynomials. Abel did not prove that any specific extension is not radical
 
hmm, strange, sources say Abel proved the theorem of natural irrationalities
or are we talking about different things
 
is the point just that Kendall's correlation coefficient converges to the Pearson correlation as the number of samples goes to infinity?
 
I don't know Pearson's correlation
Is it what you say?
Oh, you mean Spearman's?
 
$\rho_{XY}=E[(X-\mu_X)(Y-\mu_Y)]/\sigma_X \sigma_Y$
 
7:10 PM
That's just the correlation coefficient for a bivariate distribution (X, Y).
 
ah. That's Pearson's coefficient.
 
I see
I didn't know that name
 
so I'm trying to see if $P(X=Y)-P(X\neq Y)=\rho_{XY}$ in the present case
 
I don't think that's the case
 
I think I need some more assumptions for that to be true, yeah
 
7:13 PM
What I was saying is that $P(X = Y) - P(X \neq Y)$ is limit as n --> infty of Kendall's $\tau$
 
though in my case of interest it should also be the case that P(X=1,Y=-1)=P(X=-1,Y=1) and P(X=1,Y=1) = P(X=-1,Y=-1)
 
Morning
 
so the probabilities are assumed not to change if I swap the labels on +/-
lemme say that p = P(X=Y) = P(++) + P(--) = 2 P(++)=2P(--) and q = P(X!=Y) = P(+-)+P(-+)=2P(+-)=2P(-+)
hrm. I don't think this is enough either, since I don't seem to have enough info to say what E(X) would be
@BalarkaSen hmm, $E[XY] = P(X=Y)-P(X\neq Y)$
so if I can assume E[X]=E[Y]=0 and var(X)=var(Y)=1, then I would be done
 
Wait how did you get that
 
which?
 
7:22 PM
the E[XY] thing
 
ah
if X=Y, then either that's 1=1 or -1 = -1. in either case XY=1.
 
Oh you're working with this special case
 
if X != Y, they're opposite signs and so XY=-1
right.
 
I don't really care about that
 
I do :P
 
7:23 PM
Okie-dokie
Hi @Astyx
 
Hi
Wassup ?
 
E[X]=0 would require $P(X=\pm 1)=P(Y=\pm 1)=1/2$
 
H I
not much
 
in which case E[X^2]=E[Y^2] =1 as well
ahah
so I'm basically assuming that, if I look at X without knowing about Y, I just get 50/50
and under this assumption the correlation coefficient is indeed P(equal)-P(not equal)
 
What are you doing ?
 
7:26 PM
@BalarkaSen here's why I care about that: Suppose I've got 3 such variables and I form the three-by-three matrix of their pairwise correlations
 
@Astyx SemiC is correlating a dumb distribution lol
 
that'll give me $\begin{pmatrix} 1 & \rho_{12} & \rho_{13}\\ \rho_{12} & 1 & \rho_{23} \\ \rho_{13} & \rho_{23} & 1\end{pmatrix}$
with each of them lying between between -1 and 1
which, if I relabel those as x,y,z
is just the matrix $\begin{pmatrix} 1 & x & y \\ x & 1 & z \\ y & z & 1\end{pmatrix}$
 
well why should that be interesting
 
now. Does that look familiar from anything I was saying today?
 
Yes, but I don't see why just getting a matrix whose det = 0 gives your variety from something random is interesting
 
7:30 PM
well, the thing is
the correlations stuff, in the particular simple case I was saying, is precisely what got me on this track
 
i thought it was working with a trigonometric identity
 
it's that too.
it's suppose to be describing possible outcomes of certain experimental apparatuses
with the angles corresponding to the angles between the measurement systems
 
huh
 
so I can express this in two ways.
on the one hand, I can say that it's a condition on the angles.
on the other, I can say it's a requirement that the correlation matrix of that setup be singular
 
That's pretty cool
 
7:33 PM
yeah
 
I was going to write something totally dismissive until you said the last few messages
 
lol
This is quite interesting
 
So both of those viewpoints give you the same condition?
 
7:34 PM
I think so.
now here's a question: Can a correlation matrix ever fail to be positive semi-definite
 
That's pretty dope
 
I have a sneaking suspicion that the answer is no
 
Nope
You're right
 
right
now, if the correlation matrix is singular, what does that mean...
"What data produce singular correlation matrix of variables?

What multivariate data must look like in order its correlation or covariance matrix be singular matrix described above? It is when there is linear interdependances among the variables. If some variable is an exact linear combination of the other variables, with constant term allowed, the correlation and covariance matrces of the variables will be singular. "
from a cross-validated answer
 
Right, that makes sense, of course
 
7:38 PM
so the correlation matrix being singular is precisely the condition that X=aY+bZ
 
Well, at least X = aY + bZ with probability 1, I guess?
 
I don't think it'll be an exact linear dependence relation
It'll be probabilistically that or something
 
hmm, okay
 
But yeah OK
 
7:40 PM
@LeakyNun that's just not true (casus irreducibilis gives a counterexample). You need $L=\Bbb C(x_1, \dots, x_n)$ and $L= \Bbb C(t_1, \dots, t_n)$ where the $t_i$ are symmetric polynomials in the $x_i$. That's what I meant. Abel proved only theorems on general polynomials and no theorems about specific extensions of $\Bbb Q$
 
@Semiclassical So what would be the upshot of this all?
Also I'm curious to know the physics behind this
 
that's a good question
I think it goes like this
...hmmm
no, I need to think more before I try to give a grand conclusion from this
 
Okay
As long as you put me in acknowledgements of your paper on this I have no hurry to know what's up :P
 
pff
it'll be something like: "given a maximally entangled quantum state and three non-commuting observables, the three random variables corresponding to these observables will be linearly dependent."
 
7:47 PM
I see
 
that's amazing, given that I'm pretty sure what I said isn't actually quite right
 
8:09 PM
@MatheinBoulomenos I see
 
Is there any algorithm that can help solving this kind of problems: find all the $x$
 
such that?
 
@Ilya_Gazman there you go
 
for which $f(x) < c$ where $f(x)=a+bx+bx^2 \mod N$
 
"xjxkxjkhxgygyxi" -> [0, 2, 4, 8, 13]
 
8:17 PM
Where $a,b,c,x,N$ are positive integers
@LeakyNun And here is my particular question
 
Given $(df)_p (p,w) := \frac{d}{dt}\bigg|_{t=0} f(p+tw)$ and let $w=\sum_i w_ie_i$, how come

$
(df)_p (p,w) = \sum_i w_i \frac{\partial f}{\partial x_i}(p)
$

?

I see that
$$\frac{d}{dt}\bigg|_{t=0} f(p+tw)= w\frac{d}{dt}\bigg|_{t=0} f(p)$$
I don't understand how he goes from $w\frac{d}{dt}\bigg|_{t=0} f(p)$ to $\sum_i w_i \frac{\partial f}{\partial x_i}(p)$
 
9:03 PM
p is a vector
otherwise it doesn't make sense
so you need derive with respect to each component
@berrygreen
 
9:53 PM
Hi @Ted
 

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