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We introduce families of jointly symmetric, binary distributions that are generated over directed star graphs whose nodes represent variables and whose edges indicate positive dependences. The families are parametrized in terms of a single…

Methodology · Statistics 2014-07-30 N. Wermuth G. M. Marchetti P. Zwiernik

Spectral statistics of quantum systems have been studied in detail using the nearest neighbour level spacings, which for generic chaotic systems follows random matrix theory predictions. In this work, the probability density of the closest…

Chaotic Dynamics · Physics 2019-01-23 Shashi C. L. Srivastava , Arul Lakshminarayan , Steven Tomsovic , Arnd Bäcker

We relate the combinatorics of periodic generalized Dyck and Motzkin paths to the cluster coefficients of particles obeying generalized exclusion statistics, and obtain explicit expressions for the counting of paths with a fixed number of…

Mathematical Physics · Physics 2022-10-17 Li Gan , Stéphane Ouvry , Alexios P. Polychronakos

Conventional statistics begins with a model, and assigns a likelihood of obtaining any particular set of data. The opposite approach, beginning with the data and assigning a likelihood to any particular model, is explored here for the case…

Data Analysis, Statistics and Probability · Physics 2009-10-30 Timothy E. Holy

Measurements of a weighted energy density average taken in the vacuum state of a conformal field theory in $1+1$ dimensions are randomly distributed with vanishing expectation value. The probability distribution is computed in closed form…

High Energy Physics - Theory · Physics 2020-01-29 Matthew C. Anthony , Christopher J. Fewster

We introduce a series of numbers which serve as a generalization of Bernoulli, Euler numbers and binomial coefficients. Their properties are applied to solve a probability problem and suggest a statistical test for independence and…

Combinatorics · Mathematics 2013-05-09 Andrey Sarantsev

This paper introduces a new generalized polynomial chaos expansion (PCE) comprising multivariate Hermite orthogonal polynomials in dependent Gaussian random variables. The second-moment properties of Hermite polynomials reveal a weakly…

Numerical Analysis · Mathematics 2017-04-27 Sharif Rahman

Probabilistic submeasures generalizing the classical (numerical) submeasures are introduced and discussed in connection with some classes of aggregation functions. A special attention is paid to triangular norm-based probabilistic…

Classical Analysis and ODEs · Mathematics 2012-07-12 Lenka Halčinová , Ondrej Hutník , Radko Mesiar

Following the relationship between probability distribution and coherent states, for example the well known Poisson distribution and the ordinary coherent states and relatively less known one of the binomial distribution and the $su(2)$…

Quantum Physics · Physics 2016-09-08 Hong-Chen Fu , Ryu Sasaki

This document describes concisely the ubiquitous class of exponential family distributions met in statistics. The first part recalls definitions and summarizes main properties and duality with Bregman divergences (all proofs are skipped).…

Machine Learning · Computer Science 2011-05-16 Frank Nielsen , Vincent Garcia

We introduce the notion of a weighted inversion statistic on the symmetric group, and examine its distribution on each conjugacy class. Our work generalizes the study of several common permutation statistics, including the number of…

Combinatorics · Mathematics 2023-05-18 Jesse Campion Loth , Michael Levet , Kevin Liu , Eric Nathan Stucky , Sheila Sundaram , Mei Yin

The paper introduces the concept of a cluster structure to define a joint distribution of the sample size and its exchangeable random partitions. The cluster structure allows the probability distribution of the random partitions of a subset…

Methodology · Statistics 2013-10-08 Mingyuan Zhou

The polygonal distributions are a class of distributions that can be defined via the mixture of triangular distributions over the unit interval. The class includes the uniform and trapezoidal distributions, and is an alternative to the beta…

Methodology · Statistics 2017-01-18 Hien D Nguyen , Geoffrey J McLachlan

This paper puts forward a new generalized polynomial dimensional decomposition (PDD), referred to as GPDD, comprising hierarchically ordered measure-consistent multivariate orthogonal polynomials in dependent random variables. Unlike the…

Numerical Analysis · Mathematics 2018-10-30 Sharif Rahman

Gibbs-type exchangeable random partitions, which is a class of multiplicative measures on the set of positive integer partitions, appear in various contexts, including Bayesian statistics, random combinatorial structures, and stochastic…

Statistics Theory · Mathematics 2017-06-14 Shuhei Mano

Machine learning typically presupposes classical probability theory which implies that aggregation is built upon expectation. There are now multiple reasons to motivate looking at richer alternatives to classical probability theory as a…

Machine Learning · Computer Science 2024-01-30 Christian Fröhlich , Robert C. Williamson

We give criteria for boundedness of the associated Bergman-type projections on Lp-spaces on Cn with respect to generalized Gaussian weights exp(-|z|^2m). Complete characterization is obtained for 0<m<=1 and 2n/(2n-1)<m<2; partial results…

Complex Variables · Mathematics 2011-05-30 Helene Bommier-Hato , Miroslav Englis , El-Hassan Youssfi

We analyze here in details the probability to find a given number of particles in a finite volume inside a normal or superfluid finite system. This probability, also known as counting statistics, is obtained using projection operator…

Nuclear Theory · Physics 2020-01-22 Denis Lacroix , Sakir Ayik

A stochastic algorithm is proposed, finding the set of generalized means associated to a probability measure on a compact Riemannian manifold M and a continuous cost function on the product of M by itself. Generalized means include p-means…

Probability · Mathematics 2013-05-28 Marc Arnaudon , Laurent Miclo

We generalise the distribution semantics underpinning probabilistic logic programming by distilling its essential concept, the separation of a free random component and a deterministic part. This abstracts the core ideas beyond logic…

Artificial Intelligence · Computer Science 2024-05-17 Felix Weitkämper