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@Michael: hi! how are you?
nice! thanks for the answer, just accepted.
what's bothering you?
Is it called disintegration theorem?
16:28
Such things exist, yes.
I've seen something like that in Kallenberg if I remember correctly
oh, I meant the existence of the conditioning kernel
Yes, many results to that effect go under the name "disintegration theorems"
icic
There are also authors how make differences between them, but not all of them.
my question is half about the coupling, and half about the conditining so I wondered whether you worked with coupling
So I was thinking of the following: suppose I have two random variables $X,Y$ on a Polish space, where I know the distribution of $X$ being $\mu$ and the distribution of $Y$ given $X$, say $K(x,\mathrm dy)$
16:31
No, but I read a lot about conditional probability.
ok
now, I have random variables $X',Y'$ with distribution $\mu'$ and the kernel $K'$
for the first pair there is the joint distribution $\mathsf P$ and for the second $\mathsf P'$. So I'm interested in $\|\mathsf P - \mathsf P'\|$
So the first are independent? Or what do you mean by product measure.
Or do you mean their joint distribution?
well, I don't know how to call it formally. Joint distribution, thanks!
By the maximal coupling, which I'll denote by $\Bbb P$ it holds that
$$
\|\mathsf P - \mathsf P'\| = 1-2 \Bbb P(X = X', Y = Y')
$$
So now I'm interested in computing $\Bbb P(X = X',Y = Y')$ in terms of $\mu,\mu'$ and $K,K'$
I know that
$$
\Bbb P(X = X',Y = Y') = \Bbb P(X = X')\cdot\Bbb P(Y = Y'|X = X')
$$
But I even have no idea how to express $\Bbb P(X = X')$ in terms of $\mu$ and $\tilde \mu$
What relates the distributions of $(X,Y)$ and $(X',Y')$?
before the coupling, you mean?
The coupling is not unique right?
yes, but the maximal one is
16:38
They might as well be independent.
Ok.
yeah, that's why I assume that it's maximal. Otherwise I would only have the inequality
$$
\|\mathsf P - \mathsf P'\|\leq2-2\Bbb P(X = X',Y = Y')
$$
What is $\|\cdot\|$ here?
total variation norm
the equality holds only over the maximal coupling
btw, in the equality there is also 2, not 1 as I wrote above incorrectly
So, I think that
$$
$$
16:40
I see. Is there some "explicit" construction of the maximal coupling?
I found one in lecture note of den Hollander. I guess you know the guy.
I don't have it in hand, but the point is that the maximal coupling is uniquely determined by the equality above, and by the fact that
$$
\Bbb P(Z\in A,Z'\in A'|Z\neq Z') = \Bbb P(Z\in A|Z\neq Z')\cdot \Bbb P(Z'\in A'|Z\neq Z')
$$
@MichaelGreinecker no, I was also surprised that he is from Holland :)
aha, but I thought that the conditional independence outside of the diagonal can simplify things
It seems you need the dagonal to have positive measure for this, so how can you get the distribution on the diagonal?
so I think that the following holds
$$
\Bbb P(X = X') = 1-\frac12\|\mu - \mu'\|
$$
and
$$
\Bbb P(Y = Y'|X = X') = 1-\frac12\sup\limits_{x}\|K(x,\cdot) - K'(x,\cdot)\|
$$
@MichaelGreinecker I think, $\Bbb P$ is constructed to give a positive mass to the diagonal
in the formula above for the conditional independence I use $Z = (X,Y)$ and $Z' = (X',Y')$
16:51
@Ilya I don't see how you can substitute in the kernels for the measures.
@Michael: sorry, what do you precisely mean?
The maximal coupling of the distributions of $(X,Y)$ and $(X',Y')$ may not induce maximal couplings of $Y$ and $Y'$, which you seem to use when you let $\Bbb P(Y = Y'|X = X') = 1-\frac12\sup\limits_{x}\|K(x,\cdot) - K'(x,\cdot)\|$
exactly
here is my doubt - I'm neither sure that it induces the maximal coupling for $X$ and $X'$
maybe to achieve these results, I shall do a consecutive maximal coupling: first for $X$ and then (?) for $Y$ whatever it would mean
but I had a hope that $\Bbb P$ induces the maximal coupling also for the components
I would be surprised if one can solve these two issues "independently" when the variables are not independent.
I see
Ok, then I'll try push it further and I shall check out a book on couplings
thanks!
17:03
Your welcome. Sorry that I couldn't help you more
@Michael: no problem of course! and by the way, congrats on 10k+

last day (15 days later) »