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Related papers: Pairwise independent correlation gap

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We propose the formula for the number of pairs of consecutive primes $p_n, p_{n+1}<x$ separated by gap $d=p_{n+1}-p_n$ expressed directly by the number of all primes $<x$, i.e. by $\pi(x)$. As the application of this formula we formulate 7…

Number Theory · Mathematics 2018-04-24 Marek Wolf

Probabilistic independence is a useful concept for describing the result of random sampling---a basic operation in all probabilistic languages---and for reasoning about groups of random variables. Nevertheless, existing verification methods…

Programming Languages · Computer Science 2020-07-21 Gilles Barthe , Justin Hsu , Kevin Liao

Many AI researchers argue that probability theory is only capable of dealing with uncertainty in situations where a full specification of a joint probability distribution is available, and conclude that it is not suitable for application in…

Artificial Intelligence · Computer Science 2013-04-05 Linda C. van der Gaag

The paper presents a new copula based method for measuring dependence between random variables. Our approach extends the Maximum Mean Discrepancy to the copula of the joint distribution. We prove that this approach has several advantageous…

Machine Learning · Computer Science 2019-08-15 Barnabas Poczos , Zoubin Ghahramani , Jeff Schneider

An informative sampling design leads to the selection of units whose inclusion probabilities are correlated with the response variable of interest. Model inference performed on the resulting observed sample will be biased for the population…

Methodology · Statistics 2018-06-29 Matthew R. Williams , Terrance D. Savitsky

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 consider Poissonian pair correlations (PPC) for uniformly distributed sequences of random numbers with a dependency structure. More specifically, we treat two classes of dependent random variables which have widely been studied in the…

Number Theory · Mathematics 2026-01-13 Jasmin Fielder , Michael Gnewuch , Christian Weiß

Are pairs of words that tend to occur together also likely to stand in a linguistic dependency? This empirical question is motivated by a long history of literature in cognitive science, psycholinguistics, and NLP. In this work we…

Computation and Language · Computer Science 2022-05-02 Jacob Louis Hoover , Alessandro Sordoni , Wenyu Du , Timothy J. O'Donnell

In theory, the probabilistic linkage method provides two distinct advantages over non-probabilistic methods, including minimal rates of linkage error and accurate measures of these rates for data users. However, implementations can fall…

Methodology · Statistics 2019-11-06 Abel Dasylva , Arthur Goussanou , David Ajavon , Hanan Abousaleh

With a sequence of regressions, one may generate joint probability distributions. One starts with a joint, marginal distribution of context variables having possibly a concentration graph structure and continues with an ordered sequence of…

Statistics Theory · Mathematics 2017-02-03 Kayvan Sadeghi , Nanny Wermuth

A collaborative distributed binary decision problem is considered. Two statisticians are required to declare the correct probability measure of two jointly distributed memoryless process, denoted by $X^n=(X_1,\dots,X_n)$ and…

Information Theory · Computer Science 2016-04-11 Gil Katz , Pablo Piantanida , Merouane Debbah

In this paper, we characterize several classes of continuous radial basis functions that can be employed to determine whether a interaction of a probability is zero or not. These functions encompass standard independence tests but also the…

Classical Analysis and ODEs · Mathematics 2024-07-10 Jean Carlo Guella

We develope the framework of transitional conditional independence. For this we introduce transition probability spaces and transitional random variables. These constructions will generalize, strengthen and unify previous notions of…

Statistics Theory · Mathematics 2021-08-30 Patrick Forré

We study a marginal empirical likelihood approach in scenarios when the number of variables grows exponentially with the sample size. The marginal empirical likelihood ratios as functions of the parameters of interest are systematically…

Statistics Theory · Mathematics 2013-11-07 Jinyuan Chang , Cheng Yong Tang , Yichao Wu

In this paper, we develop a general theory on the coverage probability of random intervals defined in terms of discrete random variables with continuous parameter spaces. The theory shows that the minimum coverage probabilities of random…

Statistics Theory · Mathematics 2011-04-12 Xinjia Chen

The standard definition formula for probabilities of independent events is derived as a consequence of the Insufficient Reason Principle expressed as the Maximum Relative Divergence Principle for grading (order-comonotonic) functions on a…

Probability · Mathematics 2025-08-05 Alexander Dukhovny

We investigate the sample complexity of mutual information and conditional mutual information testing. For conditional mutual information testing, given access to independent samples of a triple of random variables $(A, B, C)$ with unknown…

Data Structures and Algorithms · Computer Science 2025-06-05 Jan Seyfried , Sayantan Sen , Marco Tomamichel

Elkies and McMullen [Duke Math.J.~123 (2004) 95--139] have shown that the gaps between the fractional parts of \sqrt n for n=1,\ldots,N, have a limit distribution as N tends to infinity. The limit distribution is non-standard and differs…

Number Theory · Mathematics 2013-06-28 Daniel El-Baz , Jens Marklof , Ilya Vinogradov

This paper provides constructive procedures for the indeterminacy coupling between two marginal distributions, an alternative to independence coupling. It also introduces a drawing under indeterminacy into a mixture of three independent…

Discrete Mathematics · Computer Science 2023-02-15 Pierre Bertrand , Michel Broniatowski , Jean-François Marcotorchino

We show that there can be no finite list of conditional independence relations which can be used to deduce all conditional independence implications among Gaussian random variables. To do this, we construct, for each $n> 3$ a family of $n$…

Probability · Mathematics 2007-05-23 Seth Sullivant