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We consider a superstatistical dynamical model for the 3-d movement of a Lagrangian tracer particle embedded in a high-Reynolds number turbulent flow. The analytical model predictions are in excellent agreement with recent experimental data…

Statistical Mechanics · Physics 2009-11-11 Christian Beck

We analyze the deviations from Maxwell-Boltzmann statistics found in recent experiments studying velocity distributions in two-dimensional granular gases driven into a non-equilibrium stationary state by a strong vertical vibration. We show…

Statistical Mechanics · Physics 2009-11-07 Alain Barrat , Emmanuel Trizac

A composite likelihood is an inference function derived by multiplying a set of likelihood components. This approach provides a flexible framework for drawing inference when the likelihood function of a statistical model is computationally…

Methodology · Statistics 2024-12-10 Giuseppe Alfonzetti , Ruggero Bellio , Yunxiao Chen , Irini Moustaki

To complete a previous work, the probability density functions for the errors in the center-of-gravity as positioning algorithm are derived with the usual methods of the cumulative distribution functions. These methods introduce substantial…

Instrumentation and Detectors · Physics 2021-03-08 Gregorio Landi , Giovanni E. Landi

We introduce a sharpness functional for probabilistic models that quantifies sharpness as an intrinsic property of the probability distribution. The measure is derived based on a rank-based concentration principle that tracks upward…

Methodology · Statistics 2026-04-03 Pekka Syrjänen

We reconsider a nonparametric density model based on Gaussian processes. By augmenting the model with latent P\'olya--Gamma random variables and a latent marked Poisson process we obtain a new likelihood which is conjugate to the model's…

Machine Learning · Statistics 2018-05-30 Christian Donner , Manfred Opper

This paper analyzes the compaction behavior of assemblies composed of soft (elastic) spherical particles beyond the jammed state, using three-dimensional non-smooth contact dynamic simulations. The assemblies of particles are characterized…

Soft Condensed Matter · Physics 2021-09-09 Manuel Cárdenas-Barrantes , David Cantor , Jonathan Barés , Mathieu Renouf , Emilien Azéma

A method has been found to analyze Edwards' granular contact force probability functional for a special case. As a result, the granular contact force probability density functions are obtained from first principles for this case. The…

Statistical Mechanics · Physics 2009-11-10 Philip Metzger

We find the laws for the spreading of the spatial widths (parallel and transverse to the direction of average motion) of the relativistic position probability density for a massive, spinless particle. We find that when the momentum width of…

Quantum Physics · Physics 2018-04-04 Scott E. Hoffmann

After approximate replacing of Maxwellian distribution exponent with the rational polynomial fraction we have obtained precise analytical expression for and calculated the principal value of logarithmically divergent integral in the…

Plasma Physics · Physics 2007-09-17 V. N. Soshnikov

Classical Molecular Dynamics simulations (MD) for a one-component weakly degenerate plasma are presented. Using an effective quantum pair potential (Kelbg potential), the dynamic structure factor and the dispersion of Langmuir waves are…

Strongly Correlated Electrons · Physics 2015-06-24 V. Golubnychiy , M. Bonitz , D. Kremp , M. Schlanges

Fractional Brownian motion and the fractional Langevin equation are models of anomalous diffusion processes characterized by long-range power-law correlations in time. We employ large-scale computer simulations to study these models in two…

Statistical Mechanics · Physics 2021-04-22 Thomas Vojta , Alex Warhover

In this article, we focus on the sampling of the configurational Gibbs-Boltzmann distribution, that is, the calculation of averages of functions of the position coordinates of a molecular $N$-body system modelled at constant temperature. We…

Numerical Analysis · Mathematics 2025-04-30 Benedict Leimkuhler , Charles Matthews

In the canonical ramp compression experiment, a smoothly-increasing load is applied to the surface of the sample, and the particle velocity history is measured at two or more different distances into the sample, at interfaces where the…

Computational Physics · Physics 2020-01-10 Damian C. Swift , Dayne E. Fratanduono , Richard G. Kraus , Evan A. Dowling

In a multiple testing context, we consider a semiparametric mixture model with two components where one component is known and corresponds to the distribution of $p$-values under the null hypothesis and the other component $f$ is…

Applications · Statistics 2013-04-04 Van Hanh Nguyen , Catherine Matias

We examine local Lagrangian approximations for the gravitational evolution of the density distribution function. In these approximations, the final density at a Lagrangian point q at a time t is taken to be a function only of t and of the…

Astrophysics · Physics 2015-06-24 Zacharias A. M. Protogeros , Robert J. Scherrer

Consider discrete values of functions shifted by unobserved translation effects, which are independent realizations of a random variable with unknown distribution $\mu$, modeling the variability in the response of each individual. Our aim…

Statistics Theory · Mathematics 2008-12-18 Ismael Castillo , Jean-Michel Loubes

In this paper we discuss a closed-form approximation of the likelihood functions of an arbitrary diffusion process. The approximation is based on an exponential ansatz of the transition probability for a finite time step $\Delta t$, and a…

Physics and Society · Physics 2008-12-10 Luca Capriotti

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

This paper presents a methodology and numerical algorithms for constructing accelerated gradient flows on the space of probability distributions. In particular, we extend the recent variational formulation of accelerated gradient methods in…

Machine Learning · Computer Science 2019-01-14 Amirhossein Taghvaei , Prashant G. Mehta