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Finite frames can be viewed as mass points distributed in $N$-dimensional Euclidean space. As such they form a subclass of a larger and rich class of probability measures that we call probabilistic frames. We derive the basic properties of…

Probability · Mathematics 2017-09-04 Martin Ehler , Kasso A. Okoudjou

The normal distribution is used as a unified probability distribution, however, our researcher found that it is not good agreed with the real-life dynamical system's data. We collected and analyzed representative naturally occurring data…

Dynamical Systems · Mathematics 2020-11-06 Wei Ping Cheng , Zhi Hong Zhang , Pu Wang

The Kirkwood-Dirac (KD) quasiprobability distribution can describe any quantum state with respect to the eigenbases of two observables $A$ and $B$. KD distributions behave similarly to classical joint probability distributions but can…

Quantum Physics · Physics 2024-08-02 Christopher Langrenez , David R. M. Arvidsson-Shukur , Stephan De Bièvre

The problem of joint estimation of multiple graphical models from high dimensional data has been studied in the statistics and machine learning literature, due to its importance in diverse fields including molecular biology, neuroscience…

Methodology · Statistics 2019-07-04 Peyman Jalali , Kshitij Khare , George Michailidis

We present a study of cubic surfaces from the novel perspective of positive geometry. Our positive geometries have dimension two (the surface minus its 27 lines), dimension three (its complement in 3-space), and dimension four (the moduli…

Algebraic Geometry · Mathematics 2026-05-13 Bernd Sturmfels , Simon Telen

We verify a key component of the replica symmetry breaking hypothesis put forward in the physics literature [M\'ezard and Montanari 2009] on random factor graph models. For a broad class of these models we verify that the Gibbs measure can…

Probability · Mathematics 2019-01-23 Amin Coja-Oghlan , Will Perkins

We provide a general contractibility criterion for subsets of Riemannian metrics on the disc. For instance, this result applies to the space of metrics that have positive Gauss curvature and make the boundary circle convex (or geodesic).…

Differential Geometry · Mathematics 2020-01-13 Alessandro Carlotto , Damin Wu

The family of multivariate skew-normal distributions has many interesting properties. It is shown here that these hold for a general class of skew-elliptical distributions. For this class, several stochastic representations are established…

Statistics Theory · Mathematics 2023-09-18 Chuancun Yin , Narayanaswamy Balakrishnan

Fitted probabilities from widely used Bayesian multinomial probit models can depend strongly on the choice of a base category, which is used to uniquely identify the parameters of the model. This paper proposes a novel identification…

Methodology · Statistics 2020-05-19 Lane F. Burgette , David Puelz , P. Richard Hahn

The rules of a game of dice are extended to a "hyper-die" with $n\in\mathbb{N}$ equally probable faces, numbered from 1 to $n$. We derive recursive and explicit expressions for the probability mass function and the cumulative distribution…

Probability · Mathematics 2021-03-30 Rudolf Frühwirth , Roman Malina , Winfried Mitaroff

Recent decades have seen an interest in prediction problems for which Bayesian methodology has been used ubiquitously. Sampling from or approximating the posterior predictive distribution in a Bayesian model allows one to make inferential…

Machine Learning · Statistics 2017-09-12 Giri Gopalan

Phenomena with a constrained sample space appear frequently in practice. This is the case e.g. with strictly positive data and with compositional data, like percentages and the like. If the natural measure of difference is not the absolute…

Methodology · Statistics 2008-02-20 G. Mateu-Figueras , V. Pawlowsky-Glahn , J. J. Egozcue

In this short note, we derive a new bias adjusted maximum likelihood estimate for the shape parameter of the Weibull distribution with complete data and type I censored data. The proposed estimate of the shape parameter is significantly…

Methodology · Statistics 2023-02-13 Enes Makalic , Daniel F. Schmidt

In this paper we give a systematic review of the theory of Gibbs measures of Potts model on Cayley trees (developed since 2013) and discuss many applications of the Potts model to real world situations: mainly biology, physics, and some…

Probability · Mathematics 2021-12-08 U. A. Rozikov

We study arithmetic constraints arising from the three faces meeting along the space diagonal of a rectangular cuboid. Using a propagation mechanism along this diagonal, based on the appearance of a minimal odd prime in certain triangular…

General Mathematics · Mathematics 2026-02-10 Stéphane Yelle

The idea of slicing divergences has been proven to be successful when comparing two probability measures in various machine learning applications including generative modeling, and consists in computing the expected value of a `base…

Machine Learning · Statistics 2022-01-05 Kimia Nadjahi , Alain Durmus , Lénaïc Chizat , Soheil Kolouri , Shahin Shahrampour , Umut Şimşekli

We study the distributions of money in a simple closed economic system for different types of monetary transactions. We know that for arbitrary and random sharing but locally conserving money transactions, the money distribution goes to the…

Statistical Mechanics · Physics 2009-11-07 Anirban Chakraborti

In multiple-question referendum elections, the separability problem occurs when a voter's preferences on some questions or proposals depend on the predicted outcomes of others. The notion of separability formalizes the study of…

Combinatorics · Mathematics 2018-02-08 Beth Bjorkman , Sean Gravelle , Jonathan Hodge

Two possibly unfair $n$-sided dice, both labelled $1, 2, \ldots, n$, are rolled, and the sum is recorded. How should the dice's sides be weighted so that the resulting sum is closest to the uniform distribution on $2, 3, \ldots, 2n$? We…

History and Overview · Mathematics 2023-08-03 Shamil Asgarli , Michael Hartglass , Daniel Ostrov , Byron Walden

Historically used in settings where the outcome is rare or data collection is expensive, outcome-dependent sampling is relevant to many modern settings where data is readily available for a biased sample of the target population, such as…

Methodology · Statistics 2022-07-20 Amanda Coston , Edward H. Kennedy