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A generalization of stable and casual stable probability distribution is proposed. The notion of $\go G$-casual stability can be used to introduce discrete analogues of stable distributions on the sent $\mathbb Z$ of integers. In contrary…

Probability · Mathematics 2015-06-09 Lev B. Klebanov

We propose a covariant and geometric framework to introduce space distances as they are used by astronomers. In particular, we extend the definition of space distances from the one used between events to non-test-bodies with horizons and…

General Relativity and Quantum Cosmology · Physics 2022-01-11 Salvatore Capozziello , Alice Chiappini , Lorenzo Fatibene , Andrea Orizzonte

This paper suggests a new interpretation of the Dempster-Shafer theory in terms of probabilistic interpretation of plausibility. A new rule of combination of independent evidence is shown and its preservation of interpretation is…

Artificial Intelligence · Computer Science 2017-04-07 Mieczysław Kłopotek

The proposed Habitable Worlds Observatory is intended to observe the atmospheres of nearby terrestrial exoplanets with a resolution greater than that of any previous instrument. While these observations present a substantial opportunity for…

Instrumentation and Methods for Astrophysics · Physics 2025-01-14 Theresa Fisher , Chester Harman , Estelle Janin , Megan Shabram , Shang-Min Tsai , Nicholas Wogan , Michael Wong

We study different ways of determining the mean distance $ < r_n >$ between a reference point and its $n$-th neighbour among random points distributed with uniform density in a $D$-dimensional Euclidean space. First we present a heuristic…

Probability · Mathematics 2007-05-23 Pratip Bhattacharyya , Bikas K. Chakrabarti

We introduce a distance in the space of fully-supported probability measures on one-dimensional symbolic spaces. We compare this distance to the $\bar{d}$-distance and we prove that in general they are not comparable. Our projective…

Dynamical Systems · Mathematics 2015-03-04 Liliana Trejo-Valencia , Edgardo Ugalde

For arbitrary two probability measures on real d-space with given means and variances (covariance matrices), we provide lower bounds for their total variation distance. In the one-dimensional case, a tight bound is given.

Probability · Mathematics 2022-12-27 Tomohiro Nishiyama

Estimation of time delays from a noisy and gapped data is one of the simplest data analysis problems in astronomy by its formulation. But as history of real experiments show, the work with observed data sets can be quite complex and…

Instrumentation and Methods for Astrophysics · Physics 2011-05-31 A. Hirv , N. Olspert , J. Pelt

A non-Boolean extension of the classical probability model is proposed. The non-Boolean probabilities reproduce typical quantum phenomena. The proposed model is more general and more abstract, but easier to interpret, than the quantum…

Mathematical Physics · Physics 2010-01-22 Gerd Niestegge

The probabilistic bisimilarity distance of Deng et al. has been proposed as a robust quantitative generalization of Segala and Lynch's probabilistic bisimilarity for probabilistic automata. In this paper, we present a characterization of…

Formal Languages and Automata Theory · Computer Science 2023-06-22 Giorgio Bacci , Giovanni Bacci , Kim G. Larsen , Radu Mardare , Qiyi Tang , Franck van Breugel

Manifold hypothesis states that data points in high-dimensional space actually lie in close vicinity of a manifold of much lower dimension. In many cases this hypothesis was empirically verified and used to enhance unsupervised and…

Optimization is widely used in statistics, and often efficiently delivers point estimates on useful spaces involving structural constraints or combinatorial structure. To quantify uncertainty, Gibbs posterior exponentiates the negative loss…

Methodology · Statistics 2025-07-23 Cheng Zeng , Eleni Dilma , Jason Xu , Leo L Duan

We want to approximate general multivariate probability density functions by deterministic sample sets. For optimal sampling, the closeness to the given continuous density has to be assessed. This is a difficult challenge in multivariate…

Systems and Control · Electrical Eng. & Systems 2020-01-01 Uwe D. Hanebeck

We establish quantitative comparisons between classical distances for probability distributions belonging to the class of convex probability measures. Distances include total variation distance, Wasserstein distance, Kullback-Leibler…

Probability · Mathematics 2021-12-17 Arnaud Marsiglietti , Puja Pandey

The main ideas behind a research plan to use the Wigner formulation as a bridge between classical and quantum probabilistic algorithms are presented, focusing on a particular case: the Quantum analog of Stochastic Gradient Descent in its…

Quantum Physics · Physics 2025-10-31 Jose A. Morales Escalante

The generalized second-order partial derivatives of 1/r, where r is the radial distance in 3D, are obtained using a result of the potential theory of classical analysis. Some non-spherical regularization alternatives to the standard…

Classical Physics · Physics 2015-05-19 V Hnizdo

We establish an Ergodic Theorem for lower probabilities, a generalization of standard probabilities widely used in applications. As a by-product, we provide a version for lower probabilities of the Strong Law of Large Numbers.

Functional Analysis · Mathematics 2015-08-07 Simone Cerreia-Vioglio , Fabio Maccheroni , Massimo Marinacci

We study the elusive transition from exponential to post-exponential (algebraic) decay of the probability density of a quantum particle emitted by an exponentially decaying source, in one dimension. The main finding is that the probability…

Quantum Physics · Physics 2015-05-13 E. Torrontegui , J. G. Muga , J. Martorell , D. W. L. Sprung

We propose quantum devices that can realize probabilistically different projective measurements on a qubit. The desired measurement basis is selected by the quantum state of a program register. First we analyze the phase-covariant…

Quantum Physics · Physics 2009-11-10 Jaromir Fiurasek , Miloslav Dusek

Probabilistic solvers for ordinary differential equations (ODEs) have emerged as an efficient framework for uncertainty quantification and inference on dynamical systems. In this work, we explain the mathematical assumptions and detailed…

Machine Learning · Statistics 2021-10-25 Nicholas Krämer , Nathanael Bosch , Jonathan Schmidt , Philipp Hennig