# Questions tagged [pr.probability]

Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory.

Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory.

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It has been known for long (Molloy and Reed 1995) that in a supercritical undirected configuration model, that is when $E[D(D-2)]>0$, $D$ degree of a uniform vertex, the size of the second largest ...

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Given any convex set $A\in\mathbb{R}^d$, we denote by $V(A)$ its $d$-volume. Furthermore, given any two convex sets $A_1,A_2\in\mathbb{R}^d$, we denote by $V_{A_1,A_2}$ the $d$-volume of the symmetric ...

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Consider $P(X,Y)$ discrete and a Markov chain $Y_0 \rightarrow Y_1 \rightarrow \dotsc \rightarrow Y_{L-1} \rightarrow Y_L$ with $Y_0 := Y$. The chain $Y_L \rightarrow Y_{L-1} \rightarrow \dotsc \...

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Let $X$ be a nonnegative random variable with density function $f(x)$, distribution function $F(x)$, survival function $S(x)=1-F(x)$ and finite first and second moments. Let also
$$\ell(x):=\frac{1}{...

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We denote by $V(A)$ the $d$-volume of any convex set $A$. Furthermore, given any two convex sets $A,B\in\mathbb{R}^d$, we denote by $V_{A,B}$ the $d$-volume of the symmetric difference $V\left(A \...

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My question is somehow concerning terminology on extremal graph theory.
Is there any difference concerning the notion of quasi-random graph and the notion of pseudo-random graph? My feeling is that ...

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I'm reading a proof of below theorem from this paper.
Theorem A.3. Let $\Omega$ be a locally compact normal Hausdorff space. Let $\left\{\mu_n\right\} \cup\{\mu\} \subset \mathcal{M}(\Omega)$ and ...

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Let $Z_1, Z_2, \dots$ be a Poisson point process on $[0, 1]$ with intensity function $1/z$. What is the distribution of the sum $Z = \sum_{i=1}^\infty Z_i$?
One can construct $Z_1, Z_2, \dots$ by ...

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Setup
To clarify, let constants $0 < a < b < \infty$, and $p \in \mathbb{N}$ be fixed. Further let $B \subset \mathbb{R}^{p}$ be a fixed compact support. We then define the space of bounded (...

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Lemma 1 is widely used in the stability proof of stochastic process.
Lemma 1 Assume that $\xi_k$ is a stochastic process and there is a stochastic process $V(\xi_k)$ as well as real numbers $\upsilon_{...

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I am given a quotient ring $R=\frac{\mathbb{Z}[x]}{\left< x^n +t\right>}$ for $t\in\mathbb{Z}$,
and two polynomials from $R$, $A$,$B$ and let $C$ to be there product.
Defining the norms $$\Vert ...

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Setting and definitions
This is a follow-up question on "Are infinite-variance associated processes are (BL, $\theta$)-dependent?".
The answer to that question was no in general.
So maybe a ...

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Let $X$ be a random variable on $\mathbb R^n$ and let $S_p^n := \{w \in \mathbb R^n \mid \|w\|_p = 1\}$ be the unit-sphere w.r.t to the $\ell_p$-norm in $\mathbb R^n$. We will be particularly ...

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For random variables $X_1,\dots,X_n$ with common mean $\mathbb{E}[X_i]=\mu$ and common bounds $a\leq X_i\leq b$, we have the very useful Hoeffding's inequality:
$$\mathbb{P}\left(\left|\mu -\frac1n\...

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The total variation distance between (say discrete) probability distributions, represented as vectors over their support, is defined to be
$$\Delta(\vec p,\vec q) = \frac{1}{2}\lVert \vec p-\vec q\...

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Some years ago, I found a paper with all the formulas for the balls into bins problem when the "areas" (i.e., probabilities to capture a ball) of the bins are all different. However, the ...

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Setting and definitions
Let $X = \{X(t), t \in T \}$, $T \subset \mathbb{Z}$, be an infinite-variance associated stochastic process, i.e.
$$
\text{Cov}(f(X(I)), g(X(J))) \geq 0
$$
for all finite ...

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I have been thinking about this for the last few days but I was not able to produce a definitive answer.
Take an integrable function $g$ that maps in $\mathbb{R}$ and with domain contained in $[0,M]$ (...

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Let $f \in L^2(0,1)$ and $T>0$ be fixed. How can I choose $g \in L^2(0,T)$ such that
\begin{align*}
0\equiv \mathbb{E}^x\left[f\left(X_T\right) \chi_{\tau \geqslant T}+g(T-\tau) \chi_{X_\tau=1}\...

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The Ito-Levy decomposition is well-known as a characterization of Levy processes. What does it give for the specific case of $\alpha$-stable Levy processes?

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Recently I got interested in "random Gaussian eigenvectors": Fix a large matrix $A\in \text{GL}_d(\mathbb{C})$ and denote the orthogonal projector to a fixed eigenspace by $P$. One can ...

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Suppose that $\mathcal{D}$ is a Johnson-Lindenstrauss (JL) distribution on $\mathbb{R}^{r\times n}$ ($1 \le r \le n$), meaning that there exist constants $\epsilon, \delta \in(0,1)$ such that
$$
\...

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In Chapter 3 of the textbook: An Introduction to Random Matrices, we have that for normalized GUE/GOE/GSE and ordering its eigenvalues $\lambda_1\le \lambda_2\le \cdots \le \lambda_n$, we have that
$$
...

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In introductions to abstract Wiener spaces, the sample paths usually form a Banach space; so, in particular, the sum of two sample paths is a valid sample path and also an element of the Banach space. ...

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Let $r$ and $n$ be integers such that $1 \le r \ll n$, and $\|\cdot\|$ denote the Euclidean norm of vectors or the spectral norm of matrices.
Suppose that $\mathcal{D}$ is a probability distribution ...

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Let $X$ be a random vector $\mathbb R^d$, with density $f$. For any any unit-vector $w \in \mathbb R^d$, let $\rho_w$ be the density of the random vector $X^\top w$.
Question 1. Under what minimal ...

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In a paper I am reading the authors claim that, if $B$ is a standard BM in $\mathbb{R}$ and $f\in C([0,1],\mathbb{R})$, then for any $\epsilon>0$
$$
\mathbb{P}(\sup_{t\in [0,1]}|B_t-f(t)|<\...

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Let $Y$ be an infinitely divisible (I.D.) random variable.
Let $\nu$ be any measure not necessarily finite: $\nu(\mathbb R)\leq \infty$. Suppose that $Y \sim (0, \nu,0)_0$ according to the notation on ...

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$\newcommand{\N}{\mathbb N}$Let $P$ be the set of all probability mass functions on $\N_0:=\{0\}\cup\N$, where $\N:=\{1,2,\dots\}$. Let $P_{>0}$ denote the set of all $q=(q_0,q_1,\dots)\in P$ such ...

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Many information theoretic quantities such as entropy and relative entropy appear in rate functions in large deviation theory (LDT). Is there any result in LDT that relates mutual information and rate ...

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Given two random variables $X,Y$ which are both $\mathbb{N}$-valued and have the same expected value (which is some fixed positive constant), and denote their probability mass functions by ${\bf p} = (...

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We are given a $d$-dimensional convex shape $S$ inscribed in the hypercube $[-1,1]^d$. We want find an approximation of its volume based only on a set of curves given by the intersection of the $S$ ...

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We have a pool of items, termed as item A, generated following a Poisson distribution. We use a pair of items A to produce an item B with success rate $r\in(0,1)$. My question is: is B Poisson ...

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We are given a tree $T(V,E)$ with $|V|=n$ vertices, where $V=\{v_1,v_2,\ldots, v_n\}$. We denote by $d_i$ the degree of vertex $v_i$ for all $i\in\{1,2,\ldots,n\}$.
In a sequential fashion, we select ...

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Let $\Lambda$ be a positive-definite matrix of size $n$ and let $R \ge 0$, which may depend on $n$. Consider the set $S := \{x \in \mathbb R^n \mid \|x\|_2 \le R,\,\|x\|_{\Lambda^{-1}} \le 1\}$ where $...

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Given random variables $X$,$Y$ with a joint distribution $P(X,Y)$ and another random variable $Z$, it is known that there are cases when the conditional mutual information $I(X;Y|Z)$ is greater than ...

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For more succinct description, I use the following abbreviations, i.e.,
RCPD: Regular conditional probability distribution.
RCPM: Regular conditional probability measure.
First are definitions 10.4....

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On a standard Borel (or Polish) space $X$, any probability measure $\mu$ is the push-forward of the uniform measure on $[0, 1]$ under some $f : [0, 1] \to X$.
This $f$ is not unique in general. ...

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Consider classical bond percolation on $\mathbb{Z}^d$. Each edge is included with probability $p$ and deleted with probability $1-p$. As is well known, there is a $p_c(d) \in (0,1)$ such that $p>...

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We select uniformly at random $k$ pairwise disjoint intervals from a given interval $[0,s]$ with length respectively equal to $\ell_1, \ell_2, \ldots, \ell_k\ $, i.e., we select uniformly at random $k$...

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I've seen a host of results concerning computations for $$\mathbb{E} \left[ \operatorname{tr} A^{i_1}\cdots \operatorname{tr} A^{i_j} \,\overline{\operatorname{tr} A^{k_1} \cdots \operatorname{tr} A^{...

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Let $(E,\mathscr{E})$ be a Polish space $E$ together with its Borel $\sigma$-algebra $\mathscr{E}$. Let $(\Omega, \mathscr{F},P)$ be a probability space and let $(X^{(n)})_{n \in \mathbb{N}}$ be a ...

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Let $M$ independent and identical random walks that follow the chi-squared distribution, i.e. in each step, a $X^2_1$ random variable is added. I am interested in the minimum random walk at each step. ...

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For a symmetric Gaussian random matrix $G=\{G\}_{1\le i,j \le n}$ with iid $E[G_{ij}]=0$ and $E[G_{ij}^2]=1/n$ (it is normalized), ordering its eigenvalues $\lambda_1\le \lambda_2\le\cdots \lambda_n$.
...

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The min-entropy of a random variable $X$ can often be much easier to compute than the Shannon entropy. This is because the min-entropy is simply a function of the most probable value, and sometimes, ...

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I'm reading a proof of Theorem 2.25 below from this note. First, we recall a definition and a theorem, i.e.,
Theorem 2.25 (Disintegration). Let $\left(Z, d_Z\right)$ and $\left(X, d_X\right)$ be ...

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Let $G$ be a symmetric Gaussian random matrix with iid $E[G_{ij}]=0$ and $E[G_{ij}^2]=\frac{1}{n}$, and ordering its eigenvalues $\lambda_1\le \lambda_2\le \dots \le \lambda_n$ corresponding ...

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Let $G$ be an infinite group with a finite generating set $S$. For $n \geq 1$, let $p_n$ be the probability that a random word in $S \cup S^{-1}$ of length at most $n$ represents the identity. Is it ...

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Assume we have a $M/M/\infty$ queue with arrival rate $\lambda$ and a service rate $\mu$. From Burke's theorem, the departure process of the queue is a Poisson process with rate $\lambda$.
However, ...

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Let $(\Omega,\mathcal F,P)$ be a probability space, and consider two random variables $X,Y:\Omega\rightarrow\mathbb R$. Let $X_1, X_2,\dots, X_n$ and $Y_1, Y_2, \dots, Y_m$ each be iid copies of $X$ ...