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Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory.
0
votes
Accepted
Proof of lower bound on variance
It looks to me like the authors just wanted to put in some ridiculous values which "are clearly sufficient" so that they don't have to work out the details.
The choice $\eta = c_1^{1/10}$ should ensur …
1
vote
Accepted
How to prove that is a consistent estimator?
Take $\pi^N$ with $AW(\pi^N, \pi) \leq \frac{1}{N}$, where we denote by $\mu^N$ and $\nu^N$ the marginals of $\pi^N$.
Note that by the backward induction for $AW$ (cf. here), it holds
$$
AW(\pi, \pi^N …
3
votes
Measurability of Markov kernel wrt the Borel $\sigma$-algebra generated by the weak topology
As already mentioned in the comments, this is really a standard result, see, e.g., Lemma 3.1. in Kallenberg's book.
3
votes
2
answers
346
views
General version of $d$-separation
I find the $d$-separation criterion (see, e.g., Theorem 2 here; note however the preceding definition, which basically means we are treating discrete random variables) a really useful sufficient crite …
1
vote
Building the Wasserstein space by pushforwards
Perhaps another simple argument that $\mathcal{X}$ is indeed equal to $\mathcal{W}_2(\mathbb{R}^d$):
Starting from the fact that there is a bimeasurable bijection $b : \mathbb{R}^d \rightarrow \mathbb …
1
vote
0
answers
75
views
Symmetry for bilinear optimization problem related to Gromov Wasserstein distance
The following question came up when trying to numerically solve some variants of the Gromov-Wasserstein distance.
Setting:
Let $(X_1, d_1), (X_2, d_2)$ be two compact, separable and complete metric s …
5
votes
1
answer
1k
views
Quantization of normal distribution
For $n\in\mathbb{N}$, denote by $\mathcal{Q}_n$ the set of all probability measures on $\mathbb{R}$ that are supported on at most $n$ points.
Question: Is it known which element in $\mathcal{Q}_n$ is …
2
votes
Absolutely continuous coupling of probability measures
There are various papers where this question occurs. I guess a paper which directly covers the case you are interested in is https://arxiv.org/pdf/1901.07407.pdf . Note that here, the marginals don't …
4
votes
$H(p) \le H(q) + KL(p, q)$?
Just a partial answer, but the proposed inequality doesn't hold.
Take $p = [0.2, 0.8], q = [0.1, 0.9]$.
Then $H(p) = 0.2 \log(5) + 0.8 \log(1/0.8) \approx 0.5$,
$H(q) = 0.1 \log(10) + 0.9 \log(1/0. …
3
votes
0
answers
244
views
Metric ($f$-divergence) on space of probability measures that satisfies pythagorean theorem
Let $E$ be a polish space, $\mathcal{P}(E)$ the Borel probability measures on $E$ with the topology of weak convergence and $\mathcal{Q} \subset \mathcal{P}(E)$ a convex and compact set.
First, the m …
9
votes
Accepted
Can the sum of identically distributed dependent Bernoulli trials be binomially distributed?
That's false even for $n=3$.
Denote by $B(p)$ the Bernoulli distribution which has probability $p$ of being 1 and $(1-p)$ to be 0.
Define the three Bernoulli variables $(X_1, X_2, X_3)$ by
$
X_1 \si …
2
votes
Does such a parametric distribution family exist that is "closed" with respect to addition a...
The 2013 paper "A Class of Probability Distributions that is Closed with Respect to Addition as Well as Multiplication of Independent Random Variables" by Lennard Bondesson is closely related to your …
0
votes
0
answers
57
views
Absolute continuity of probability measures determined by dependence structure
We are on $\mathbb{R}^d$ with Borel $\sigma$-algebra. Let $\mu_1, ..., \mu_d$ be probability measures on $\mathbb{R}$ and $\Pi(\mu_1, \mu_2, ..., \mu_d)$ be the set of probability measures on $\mathbb …
1
vote
Accepted
Draw samples from distribitions in the neighborhood of a fixed distribution
Maybe to add to the point of calculating $\max_{P_\varepsilon} \mathbb{E}_{P_\varepsilon}[f]$: I will write this a bit more in line with the literature I will refer to. Let $(X, d)$ be some polish spa …
8
votes
Does this moment inequality hold for any probability measure on the positive real line?
It doesn't hold.
Note that if one chooses $P= \frac{1}{n^2} \delta_n + \frac{n^2-1}{n^2} \delta_x$ with $x$ chosen such that $\mu = 1$, i.e. $x = \frac{n^2-n}{n^2-1}$, then $\mu_3 \sim n$ asymptotical …