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This article establishes a bilinear embedding for second-order divergence-form operators with complex coefficients, characterized by the simultaneous presence of first-order terms and negative potentials. This work provides a further…

Analysis of PDEs · Mathematics 2026-05-15 Lorenzo Luciano Morelato , Andrea Poggio

Growth in both size and complexity of modern data challenges the applicability of traditional likelihood-based inference. Composite likelihood (CL) methods address the difficulties related to model selection and computational intractability…

Statistics Theory · Mathematics 2017-09-12 Zhendong Huang , Davide Ferrari

Permutation tests are a distribution free way of performing hypothesis tests. These tests rely on the condition that the observed data are exchangeable among the groups being tested under the null hypothesis. This assumption is easily…

Methodology · Statistics 2017-12-14 Daniell Toth

In this paper, we propose a general method for testing inequality restrictions on nonparametric functions. Our framework includes many nonparametric testing problems in a unified framework, with a number of possible applications in auction…

Statistics Theory · Mathematics 2015-06-18 Sokbae Lee , Kyungchul Song , Yoon-Jae Whang

We construct several new spaces of quantum sequences and their quantum families of maps in sense of So{\l}tan. Then, we introduce noncommutative distributional symmetries associated with these quantum maps and study simple relations between…

Operator Algebras · Mathematics 2015-05-12 Weihua Liu

We introduce a canonical notion of entropy for polynomials analogue to that of random variables in probability. We prove that entropy increases smoothly with respect to finite free addition. In particular we get the new inequality : $…

Classical Analysis and ODEs · Mathematics 2023-11-07 Aurelien Gribinski

We discuss the usefulness of quantum cloning and present examples of quantum computation tasks for which cloning offers an advantage which cannot be matched by any approach that does not resort to it. In these quantum computations, we need…

Quantum Physics · Physics 2007-05-23 Gao Ting , Yan Feng-Li , Wang Zhi-Xi

We introduce the problem of \emph{entropy equivalence testing} for probability distributions, a relaxation of the well-studied closeness testing problem, where the distribution testing algorithm is now only required to distinguish, given…

Data Structures and Algorithms · Computer Science 2026-05-25 Clément L. Canonne , Yash Pote , Jonathan Scarlett , Joy Qiping Yang

We describe a novel way to represent the probability distribution of a random binary string as a mixture having a maximally weighted component associated with independent (though not necessarily identically distributed) Bernoulli…

Probability · Mathematics 2020-04-21 Antony Pearson , Manuel E. Lladser

We give a proof of the Khintchine inequalities in non-commutative $L_p$-spaces for all $0< p<1$. These new inequalities are valid for the Rademacher functions or Gaussian random variables, but also for more general sequences, e.g. for the…

Operator Algebras · Mathematics 2017-10-02 Gilles Pisier , Eric Ricard

We establish a central limit theorem for the sum of $\epsilon$-independent random variables, extending both the classical and free probability setting. Central to our approach is the use of graphon limits to characterize the limiting…

Probability · Mathematics 2024-12-02 Guillaume Cébron , Patrick Oliveira Santos , Pierre Youssef

Testing independence among a number of (ultra) high-dimensional random samples is a fundamental and challenging problem. By arranging $n$ identically distributed $p$-dimensional random vectors into a $p \times n$ data matrix, we investigate…

Statistics Theory · Mathematics 2017-03-28 Xi Chen , Weidong Liu

We study the relative error of the state-dependent N=>L cloning. A copying transformation and dimension of state space are not specified. Only the unitarity of quantum mechanical transformations is used. The proposed approach is based on…

Quantum Physics · Physics 2007-05-23 A. E. Rastegin

We introduce a finite version of free probability for rectangular matrices that amounts to operations on singular values of polynomials. We show that we can replicate the transforms from free probability, and that asymptotically there is…

Probability · Mathematics 2023-10-25 Aurelien Gribinski

We consider the problem of parametric statistical inference when likelihood computations are prohibitively expensive but sampling from the model is possible. Several so-called likelihood-free methods have been developed to perform inference…

Machine Learning · Statistics 2020-09-14 Owen Thomas , Ritabrata Dutta , Jukka Corander , Samuel Kaski , Michael U. Gutmann

We define a p-norm in the context of quantum random variables, measurable operator-valued functions with respect to a positive operator-valued measure. This norm leads to a operator-valued L^p space that is shown to be complete. Various…

Functional Analysis · Mathematics 2021-08-31 Christopher Ramsey , Adam Reeves

The extended de Finetti theorem characterizes exchangeable infinite random sequences as conditionally i.i.d. and shows that the apparently weaker distributional symmetry of spreadability is equivalent to exchangeability. Our main result is…

Operator Algebras · Mathematics 2008-06-24 Claus Köstler

We analyse notion of independence in the EPR-Bohm framework by using comparative analysis of independence in conventional and frequency probability theories. Such an analysis is important to demonstrate that Bell's inequality was obtained…

Quantum Physics · Physics 2015-06-26 Andrei Khrennikov

For the principal eigenvalue of discrete weighted $p$-Laplacian on the set of nonnegative integers, the convergence of an approximation procedure and the inverse iteration is proved. Meanwhile, in the proof of the convergence, the…

Probability · Mathematics 2019-03-11 Yue-Shuang Li

Likelihood-free inference refers to inference when a likelihood function cannot be explicitly evaluated, which is often the case for models based on simulators. Most of the literature is based on sample-based `Approximate Bayesian…

Machine Learning · Statistics 2018-11-22 Conor Durkan , George Papamakarios , Iain Murray