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We consider three new classes of exponential dispersion models of discrete probability distributions which are defined by specifying their variance functions in their mean value parameterization. In a previous paper (Bar-Lev and Ridder,…

Methodology · Statistics 2020-04-01 Shaul K. Bar-Lev , Ad Ridder

Non-homogeneous Poisson processes are used in a wide range of scientific disciplines, ranging from the environmental sciences to the health sciences. Often, the central object of interest in a point process is the underlying intensity…

Methodology · Statistics 2022-02-11 Tin Lok James Ng , Andrew Zammit-Mangion

Robust estimation has played an important role in statistical and machine learning. However, its applications to functional linear regression are still under-developed. In this paper, we focus on Huber's loss with a diverging robustness…

Statistics Theory · Mathematics 2024-09-18 Ling Peng , Xiaohui Liu , Heng Lian

We derive the characteristic function of stochastic functionals of a random walk whose position is reset to the origin at random times drawn from a general probability distribution. We analyze the long-time behavior and obtain the temporal…

Statistical Mechanics · Physics 2025-07-09 V. Méndez , R. Flaquer-Galmés

It is shown by Monte Carlo method that the finite size scaling (FSS) holds in the two dimensional random-coupled Ising ferromagnet. It is also demonstrated that the form of universal FSS function constructed via novel FSS scheme depends on…

Statistical Mechanics · Physics 2009-10-31 Jae-Kwon Kim

Context: Two-point correlation functions are used throughout cosmology as a measure for the statistics of random fields. When used in Bayesian parameter estimation, their likelihood function is usually replaced by a Gaussian approximation.…

Cosmology and Nongalactic Astrophysics · Physics 2011-10-07 David Keitel , Peter Schneider

A strong-coupling expansion for the Green's functions, self-energies and correlation functions of the Bose Hubbard model is developed. We illustrate the general formalism, which includes all possible inhomogeneous effects in the formalism,…

Other Condensed Matter · Physics 2009-07-09 J. K. Freericks , H. R. Krishnamurthy , Yasuyuki Kato , Naoki Kawashima , Nandini Trivedi

Considered are the large $N$, or large intensity, forms of the distribution of the length of the longest increasing subsequences for various models. Earlier work has established that after centring and scaling, the limit laws for these…

Mathematical Physics · Physics 2022-06-09 Peter J. Forrester , Anthony Mays

Random fields are useful mathematical tools for representing natural phenomena with complex dependence structures in space and/or time. In particular, the Gaussian random field is commonly used due to its attractive properties and…

Mobility entropy is proposed to measure predictability of human movements, based on which, the upper and lower bound of prediction accuracy is deduced, but corresponding mathematical expressions of prediction accuracy keeps yet open. In…

Social and Information Networks · Computer Science 2019-01-29 Lu Liu , Wuyang Zhou , Sihai Zhang , Wei Cai

The joint probability distribution function (PDF) of the density within multiple concentric spherical cells is considered. It is shown how its cumulant generating function can be obtained at tree order in perturbation theory as the Legendre…

Cosmology and Nongalactic Astrophysics · Physics 2014-11-19 Francis Bernardeau , Christophe Pichon , Sandrine Codis

Generalized dimensions of multifractal measures are usually seen as static objects, related to the scaling properties of suitable partition functions, or moments of measures of cells. When these measures are invariant for the flow of a…

Dynamical Systems · Mathematics 2019-10-02 Théophile Caby , Davide Faranda , Giorgio Mantica , Sandro Vaienti , Pascal Yiou

We provide finite-sample distribution approximations, that are uniform in the parameter, for inference in linear mixed models. Focus is on variances and covariances of random effects in cases where existing theory fails because their…

Statistics Theory · Mathematics 2025-07-29 Karl Oskar Ekvall , Matteo Bottai

Samples of dynamic or time-varying networks and other random object data such as time-varying probability distributions are increasingly encountered in modern data analysis. Common methods for time-varying data such as functional data…

Methodology · Statistics 2024-07-23 Paromita Dubey , Hans-Georg Müller

It is well known that generalized models is attracting the attention of researchers in recent times because of their flexibilities. Particularly, the logistic model has been generalized and applied by many authors while the half logistic…

Statistics Theory · Mathematics 2008-12-18 A. K. Olapade

Level and wavefunction statistics have been studied for two dimensional clusters of the square lattice in the presence of random magnetic fluxes. Fluxes traversing lattice plaquettes are distributed uniformly between - (1/2) Phi_0 and (1/2)…

Condensed Matter · Physics 2009-10-28 J. A. Verges

Based on recent advancements in using machine learning for classical density functional theory for systems with one-dimensional, planar inhomogeneities, we propose a machine learning model for application in two dimensions (2D) akin to…

Statistical Mechanics · Physics 2025-05-22 Felix Glitsch , Jens Weimar , Martin Oettel

We give an introductory account of the recent hyperdensity functional theory for the equilibrium statistical mechanics of soft matter systems [F. Samm\"uller et al., Phys. Rev. Lett. 133, 098201 (2024); 10.1103/PhysRevLett.133.098201].…

Soft Condensed Matter · Physics 2025-02-27 Florian Sammüller , Matthias Schmidt

Analytical study of the distribution of phase of the transmission coefficient through 1D disordered absorbing system is presented. The phase is shown to obey approximately Gaussian distribution. An explicit expression for the variance is…

Disordered Systems and Neural Networks · Physics 2009-10-28 V. Freilikher , M. Pustilnik

We introduce a novel generative formulation of deep probabilistic models implementing "soft" constraints on their function dynamics. In particular, we develop a flexible methodological framework where the modeled functions and derivatives…

Machine Learning · Statistics 2018-06-19 Marco Lorenzi , Maurizio Filippone
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