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Let $\mathbf{W}=(W_1,W_2,...,W_k)$ be a random vector with nonnegative coordinates having nonzero and finite variances. We prove concentration inequalities for $\mathbf{W}$ using size biased couplings that generalize the previous univariate…

Probability · Mathematics 2013-10-22 Subhankar Ghosh , Umit Islak

Consider a uniform expanders family G_n with a uniform bound on the degrees. It is shown that for any p and c>0, a random subgraph of G_n obtained by retaining each edge, randomly and independently, with probability p, will have at most one…

Probability · Mathematics 2007-05-23 Noga Alon , Itai Benjamini , Alan Stacey

We establish the sharpness of the percolation phase transition for a class of infinite-range weighted random connection models. The vertex set is given by a marked Poisson point process on $\mathbb{R}^d$ with intensity $\lambda>0$, where…

Probability · Mathematics 2025-12-29 Alejandro Caicedo , Leonid Kolesnikov

We show an extension of Sanov's theorem on large deviations, controlling the tail probabilities of i.i.d. random variables with matching concentration and anti-concentration bounds. This result has a general scope, applies to samples of any…

Machine Learning · Computer Science 2021-10-12 Akshay Balsubramani

A random variable $\xi$ has a {\it light-tailed} distribution (for short: is light-tailed) if it possesses a finite exponential moment, $\E \exp (\lambda \xi) <\infty$ for some $\lambda >0$, and has a {\it heavy-tailed} distribution (is…

Probability · Mathematics 2025-09-09 Sergey Foss , Anton Tarasenko , Georgiy Krivtsov

We apply the Law of Total Probability to the construction of scale-invariant probability distribution functions (pdfs), and require that probability measures be dimensionless and unitless under a continuous change of scales. If the…

Data Analysis, Statistics and Probability · Physics 2016-04-27 J. L. Friar , T. Goldman , J. Perez-Mercader

The Chernoff bound is a well-known tool for obtaining a high probability bound on the expectation of a Bernoulli random variable in terms of its sample average. This bound is commonly used in statistical learning theory to upper bound the…

Machine Learning · Statistics 2022-05-18 Andrew Y. K. Foong , Wessel P. Bruinsma , David R. Burt

We study long-range Bernoulli percolation on $\mathbb{Z}^d$ in which each two vertices $x$ and $y$ are connected by an edge with probability $1-\exp(-\beta \|x-y\|^{-d-\alpha})$. It is a theorem of Noam Berger (CMP, 2002) that if…

Probability · Mathematics 2021-02-15 Tom Hutchcroft

In this work we derive a variant of the classic Glivenko-Cantelli Theorem, which asserts uniform convergence of the empirical Cumulative Distribution Function (CDF) to the CDF of the underlying distribution. Our variant allows for tighter…

Machine Learning · Computer Science 2017-11-07 Noga Alon , Moshe Babaioff , Yannai A. Gonczarowski , Yishay Mansour , Shay Moran , Amir Yehudayoff

We consider dynamical systems on a finite measure space fulfilling a spectral gap property and Birkhoff sums of a non-negative, non-integrable observable. For such systems we generalize strong laws of large numbers for intermediately…

Dynamical Systems · Mathematics 2019-09-04 Marc Kesseböhmer , Tanja Schindler

We derive a necessary and sufficient condition for the sum of M independent continuous random variables modulo 1 to converge to the uniform distribution in L^1([0,1]), and discuss generalizations to discrete random variables. A consequence…

Probability · Mathematics 2010-09-15 Steven J. Miller , Mark J. Nigrini

Almost 10 years ago, Impagliazzo and Kabanets (2010) gave a new combinatorial proof of Chernoff's bound for sums of bounded independent random variables. Unlike previous methods, their proof is constructive. This means that it provides an…

Discrete Mathematics · Computer Science 2020-03-03 Wolfgang Mulzer , Natalia Shenkman

Under the assumption of the Riemann Hypothesis, the Linear Independence Hypothesis, and a bound on negative discrete moments of the Riemann zeta function, we prove the existence of a limiting logarithmic distribution of the normalisation of…

Number Theory · Mathematics 2013-01-14 Peter Humphries

We prove local and global inverse theorems for general $3$-wise correlations over pairwise-connected distributions. Let $\mu$ be a distribution over $\Sigma \times \Gamma \times \Phi$ such that the supports of $\mu_{xy}$, $\mu_{xz}$, and…

Computational Complexity · Computer Science 2024-11-25 Amey Bhangale , Subhash Khot , Yang P. Liu , Dor Minzer

Biggins [Uniform convergence of martingales in the branching random walk. {\em Ann. Probab.}, 20(1):137--151, 1992] proved local uniform convergence of additive martingales in $d$-dimensional supercritical branching random walks at complex…

Probability · Mathematics 2016-11-17 Konrad Kolesko , Matthias Meiners

On a locally finite, infinite tree $T$, let $p_c(T)$ denote the critical probability for Bernoulli percolation. We prove that every positively associated, finite-range dependent percolation model on $T$ with marginals $p > p_c(T)$ must…

Probability · Mathematics 2024-05-14 Laurin Köhler-Schindler , Aurelio L. Sulser

Consider a discrete-time one-dimensional supercritical branching random walk. We study the probability that there exists an infinite ray in the branching random walk that always lies above the line of slope $\gamma-\epsilon$, where $\gamma$…

Probability · Mathematics 2010-02-16 Nina Gantert , Yueyun Hu , Zhan Shi

In this paper, we study dependence uncertainty and the resulting effects on tail risk measures, which play a fundamental role in modern risk management. We introduce the notion of a regular dependence measure, defined on multi-marginal…

Risk Management · Quantitative Finance 2024-06-28 Corrado De Vecchi , Max Nendel , Jan Streicher

Large deviation inequalities for ergodic sums is an important subject since the seminal contribution of Bernstein for independent random variables with finite variances, followed by the Chernoff method and the Hoefding result for…

Probability · Mathematics 2025-12-12 Miguel Abadi

We consider correlated random variables $X_1,\dots,X_n$ taking values in $\{0,1\}$ such that, for any permutation $\pi$ of $\{1,\dots,n\}$, the random vectors $(X_1,\dots,X_n)$ and $(X_{\pi(1)},\dots,X_{\pi(n)})$ have the same distribution.…

Statistical Mechanics · Physics 2015-06-22 Max Jauregui , Constantino Tsallis