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We study stable like behaviour in first order theories without the independence property. We introduce generically stable measures, give characterizatiions, and show their ubiquity. We also introduce generic compact domination. We also…

Logic · Mathematics 2010-02-26 Ehud Hrushovski , Anand Pillay , Pierre Simon

The demonstration and use of Bell-nonlocality, a concept that is fundamentally striking and is at the core of applications in device independent quantum information processing, relies heavily on the assumption of measurement independence,…

Quantum Physics · Physics 2016-05-13 Gilles Pütz , Nicolas Gisin

Over the last couple of decades, several copula based methods have been proposed in the literature to test for the independence among several random variables. But these existing tests are not invariant under monotone transformations of the…

Statistics Theory · Mathematics 2019-11-15 Angshuman Roy , Anil Ghosh , Alok Goswami , C. A. Murthy

In this paper, the defining properties of a valid measure of the dependence between two random variables are reviewed and complemented with two original ones, shown to be more fundamental than other usual postulates. While other popular…

Methodology · Statistics 2019-12-03 Gery Geenens , Pierre Lafaye de Micheaux

We continue the study of Carleman-Sobolev classes from previous joint work with G. Behm. We consider spaces denoted by $W_\mathcal{M}^p$, defined as abstract completions of sets of smooth functions with respect to a weighted…

Classical Analysis and ODEs · Mathematics 2017-10-31 Aron Wennman

Cyclic monotone independence is an algebraic notion of noncommutative independence, introduced in the study of multi-matrix random matrix models with small rank. Its algebraic form turns out to be surprisingly close to monotone…

Operator Algebras · Mathematics 2024-11-12 Benoît Collins , Felix Leid , Noriyoshi Sakuma

Measuring and testing dependence between complex objects is of great importance in modern statistics. Most existing work relied on the distance between random variables, which inevitably required the moment conditions to guarantee the…

Methodology · Statistics 2023-04-19 Yilin Zhang , Songshan Yang

Measuring dependence between two events, or equivalently between two binary random variables, amounts to expressing the dependence structure inherent in a $2\times 2$ contingency table in a real number between $-1$ and $1$. Countless such…

Methodology · Statistics 2025-11-13 Marc-Oliver Pohle , Timo Dimitriadis , Jan-Lukas Wermuth

The paper presents new metrics to quantify and test for (i) the equality of distributions and (ii) the independence between two high-dimensional random vectors. We show that the energy distance based on the usual Euclidean distance cannot…

Methodology · Statistics 2019-10-01 Shubhadeep Chakraborty , Xianyang Zhang

We propose a new conditional dependence measure and a statistical test for conditional independence. The measure is based on the difference between analytic kernel embeddings of two well-suited distributions evaluated at a finite set of…

Machine Learning · Statistics 2022-06-17 Meyer Scetbon , Laurent Meunier , Yaniv Romano

We introduce a novel measure of dependence that captures the extent to which a random variable $Y$ is determined by a random vector $X$. The measure equals zero precisely when $Y$ and $X$ are independent, and it attains one exactly when $Y$…

Statistics Theory · Mathematics 2026-01-14 Mona Azadkia , Pouya Roudaki

Subobject independence as morphism co-possibility has recently been defined in [2] and studied in the context of algebraic quantum field theory. This notion of independence is handy when it comes to systems coming from physics, but when…

Category Theory · Mathematics 2023-06-21 Zalán Gyenis , Alexa Gopaulsingh , Övge Öztürk

Following our previous work on copula-based nonsymmetric bivariate dependence measures, we propose a new set of conditions on nonsymmetric multivariate dependence measures which characterize both independence and complete dependence of one…

Methodology · Statistics 2015-12-04 Hui Li

We study general methods to build forking-like notions in the framework of tame abstract elementary classes (AECs) with amalgamation. We show that whenever such classes are categorical in a high-enough cardinal, they admit a good frame: a…

Logic · Mathematics 2016-08-29 Sebastien Vasey

We examine the statistical number of states, from which statistical entropy can be derived, and we show that it is an explicit function of the metric and thus observer dependent. We find a constraint on a transformation of the metric that…

General Relativity and Quantum Cosmology · Physics 2014-07-22 Judy Kupferman

In an independence model, the triplets that represent conditional independences between singletons are called elementary. It is known that the elementary triplets represent the independence model unambiguously under some conditions. In this…

Machine Learning · Statistics 2016-12-06 Jose M. Peña

We show that the class $\mathscr{B}$, of discrete groups which satisfy the conclusion of Popa's Cocycle Superrigidity Theorem for Bernoulli actions, is invariant under measure equivalence. We generalize this to the setting of discrete…

Dynamical Systems · Mathematics 2021-06-08 Lewis Bowen , Robin Tucker-Drob

We study abstract elementary classes (AECs) that, in $\aleph_0$, have amalgamation, joint embedding, no maximal models and are stable (in terms of the number of orbital types). Assuming a locality property for types, we prove that such…

Logic · Mathematics 2018-05-31 Saharon Shelah , Sebastien Vasey

In this paper we construct the new coefficient which allows to measure quantitatively the independence of the two discrete random variables. The new inequalities for the matrices with non-negative elements are found

Probability · Mathematics 2010-08-04 E. A. Yanovich

We introduce an independence criterion based on entropy regularized optimal transport. Our criterion can be used to test for independence between two samples. We establish non-asymptotic bounds for our test statistic and study its…

Machine Learning · Statistics 2022-04-21 Lang Liu , Soumik Pal , Zaid Harchaoui