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A distributional symmetry is invariance of a distribution under a group of transformations. Exchangeability and stationarity are examples. We explain that a result of ergodic theory provides a law of large numbers: If the group satisfies…

Statistics Theory · Mathematics 2021-11-30 Morgane Austern , Peter Orbanz

Central limit theorems (CLTs) for high-dimensional random vectors with dimension possibly growing with the sample size have received a lot of attention in the recent times. Chernozhukov et al. (2017) proved a Berry--Esseen type result for…

Statistics Theory · Mathematics 2019-06-26 Arun Kumar Kuchibhotla , Somabha Mukherjee , Debapratim Banerjee

A judicious application of the Berry-Esseen theorem via suitable Augustin information measures is demonstrated to be sufficient for deriving the sphere packing bound with a prefactor that is…

Information Theory · Computer Science 2020-05-12 Baris Nakiboglu

We seek an entropy estimator for discrete distributions with fully empirical accuracy bounds. As stated, this goal is infeasible without some prior assumptions on the distribution. We discover that a certain information moment assumption…

Information Theory · Computer Science 2022-12-27 Doron Cohen , Aryeh Kontorovich , Aaron Koolyk , Geoffrey Wolfer

A pattern of a sequence is a sequence of integer indices with each index describing the order of first occurrence of the respective symbol in the original sequence. In a recent paper, tight general bounds on the block entropy of patterns of…

Information Theory · Computer Science 2007-11-15 Gil I. Shamir

We obtain Berry-Esseen-type bounds for the sum of random variables with a dependency graph and uniformly bounded moments of order $\delta \in (2,\infty]$ using a Fourier transform approach. Our bounds improve the state-of-the-art in the…

Probability · Mathematics 2023-03-01 Maximilian Janisch , Thomas Lehéricy

It is expected that the statistical fluctuations of local observables in large quantum systems obey the central limit theorem, and approximate a normal distribution as their size grows. Here, we prove a version of the Berry-Esseen theorem…

In most data-scientific approaches, the principle of Maximum Entropy (MaxEnt) is used to a posteriori justify some parametric model which has been already chosen based on experience, prior knowledge or computational simplicity. In a…

Methodology · Statistics 2022-06-29 Orestis Loukas , Ho Ryun Chung

Weakly almost i.i.d. quantum sources are sequences of multipartite states whose fixed-size marginals converge, on average, to tensor powers of a reference state, while allowing arbitrary global correlations and entanglement. We establish…

Quantum Physics · Physics 2026-05-20 Nilanjana Datta

We establish both uniform and nonuniform error bounds of the Berry-Esseen type in normal approximation under local dependence. These results are of an order close to the best possible if not best possible. They are more general or sharper…

Probability · Mathematics 2007-05-23 Louis H. Y. Chen , Qi-Man Shao

Within a framework of utmost generality, we show that the entropy maximization procedure with linear constraints uniquely leads to the Shannon-Boltzmann-Gibbs entropy. Therefore, the use of this procedure with linear constraints should not…

Statistical Mechanics · Physics 2018-05-01 Thomas Oikonomou , G. Baris Bagci

A well-known result across information theory, machine learning, and statistical physics shows that the maximum entropy distribution under a mean constraint has an exponential form called the Gibbs-Boltzmann distribution. This is used for…

Machine Learning · Computer Science 2020-06-26 Amir R. Asadi , Emmanuel Abbe

The entropy accumulation theorem states that the smooth min-entropy of an $n$-partite system $A = (A_1, \ldots, A_n)$ is lower-bounded by the sum of the von Neumann entropies of suitably chosen conditional states up to corrections that are…

Quantum Physics · Physics 2019-07-23 Frédéric Dupuis , Omar Fawzi

Sharp, nonasymptotic bounds are obtained for the relative entropy between the distributions of sampling with and without replacement from an urn with balls of $c\geq 2$ colors. Our bounds are asymptotically tight in certain regimes and,…

Probability · Mathematics 2026-01-14 Oliver Johnson , Lampros Gavalakis , Ioannis Kontoyiannis

Tight bounds on the block entropy of patterns of sequences generated by independent and identically distributed (i.i.d.) sources are derived. A pattern of a sequence is a sequence of integer indices with each index representing the order of…

Information Theory · Computer Science 2007-11-14 Gil I. Shamir

his study presents a novel technique to estimate the computational complexity of sequential decoding using the Berry-Esseen theorem. Unlike the theoretical bounds determined by the conventional central limit theorem argument, which often…

Information Theory · Computer Science 2007-08-20 Po-Ning Chen , Yunghsiang S. Han , Carlos R. P. Hartmann , Hong-Bin Wu

We derive a lower bound on the smallest output entropy that can be achieved via vector quantization of a $d$-dimensional source with given expected $r$th-power distortion. Specialized to the one-dimensional case, and in the limit of…

Information Theory · Computer Science 2017-03-27 Tobias Koch , Gonzalo Vazquez-Vilar

Let $\{{X}_k\}_{k\geq\mathbb{Z}}$ be a stationary sequence. Given $p\in(2,3]$ moments and a mild weak dependence condition, we show a Berry-Esseen theorem with optimal rate $n^{p/2-1}$. For $p\geq4$, we also show a convergence rate of…

Probability · Mathematics 2020-07-28 Moritz Jirak

There has been a resurgence of interest in incomplete U-statistics that only sum over a subset of kernel evaluations, due to their computational efficiency and asymptotic normality which can be leveraged to quantify the uncertainty of…

Statistics Theory · Mathematics 2026-01-14 Dennis Leung

Let $r=r(n)$ be a sequence of integers such that $r\leq n$ and let $X_1,\ldots,X_{r+1}$ be independent random points distributed according to the Gaussian, the Beta or the spherical distribution on $\mathbb{R}^n$. Limit theorems for the…

Probability · Mathematics 2017-08-03 Julian Grote , Zakhar Kabluchko , Christoph Thäle