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We consider a multinomial distribution, where the number of cells increases and the cell-probabilities decreases as the number of observations grows. The probabilities of large deviations of statistics, which has form of a sum of Borel…

Probability · Mathematics 2022-05-09 Sherzod M. Mirakhmedov

Diffusion models are a class of probabilistic generative models that have been widely used as a prior for image processing tasks like text conditional generation and inpainting. We demonstrate that these models can be adapted to make…

Machine Learning · Computer Science 2023-06-14 Marc Finzi , Anudhyan Boral , Andrew Gordon Wilson , Fei Sha , Leonardo Zepeda-Núñez

We explore the influence of finite volume effects on baryon number fluctuations in a non-perturbative chiral model. In order to account for soft modes, we use the functional renormalization group in a finite volume, using a smooth regulator…

High Energy Physics - Phenomenology · Physics 2017-03-22 Gabor A. Almasi , Robert D. Pisarski , Vladimir V. Skokov

We consider a class of real numbers, a subset of irrational numbers and certain mathematical constants, for which the elements in the simple continued fraction appears to be random. As an illustrative example, one can consider $\pi = \{x_0,…

Statistical Mechanics · Physics 2020-02-19 Avinash Chand Yadav

We show that an analogy between crowding in fluid and jammed phases of hard spheres captures the density dependence of the kissing number for a family of numerically generated jammed states. We extend this analogy to jams of mixtures of…

We use the spherical evolution approximation to investigate nonlinear evolution from the non-Gaussian initial conditions characteristic of the local f_nl model. We provide an analytic formula for the nonlinearly evolved probability…

Astrophysics · Physics 2009-11-13 Tsz Yan Lam , Ravi K. Sheth

We present a finite volume scheme for modeling the diffusion of charged particles, specifically ions, in constrained geometries using a degenerate Poisson-Nernst-Planck system with size exclusion yielding cross-diffusion. Our method…

Numerical Analysis · Mathematics 2026-03-04 Clément Cancès , Maxime Herda , Annamaria Massimini

We propose a new class of discrete generalized linear models based on the class of Poisson-Tweedie factorial dispersion models with variance of the form $\mu + \phi\mu^p$, where $\mu$ is the mean, $\phi$ and $p$ are the dispersion and…

We study finite volume effects within the Polyakov loop Nambu-Jona-Lasinio model for two light and one heavy quarks at vanishing baryon chemical potential and finite temperatures. We include three different Polyakov loop potentials and…

High Energy Physics - Phenomenology · Physics 2018-08-24 A. G. Grunfeld , G. Lugones

We develop a simple computational model for cell boundary evolution in plastic deformation. We study the cell boundary size distribution and cell boundary misorientation distribution that experimentally have been found to have scaling forms…

Materials Science · Physics 2013-05-29 James P. Sethna , Valerie R. Coffman , Eugene Demler

By studying the statistics of recurrence intervals, $\tau$, between volatilities of Internet traffic rate changes exceeding a certain threshold $q$, we find that the probability distribution functions, $P_{q}(\tau)$, for both byte and…

Data Analysis, Statistics and Probability · Physics 2009-10-01 Shi-Min Cai , Zhong-Qian Fu , Tao Zhou , Jun Gu , Pei-Ling Zhou

Marginal structural models fit via inverse probability of treatment weighting are commonly used to control for confounding when estimating causal effects from observational data. When planning a study that will be analyzed with marginal…

Applications · Statistics 2020-03-16 Bonnie E. Shook-Sa , Michael G. Hudgens

The probabilities of causation are commonly used to solve decision-making problems. Tian and Pearl derived sharp bounds for the probability of necessity and sufficiency (PNS), the probability of sufficiency (PS), and the probability of…

Artificial Intelligence · Computer Science 2022-10-12 Ang Li , Ruirui Mao , Judea Pearl

We examine the cosmological redshift-space distortion effect on the power spectrum of the objects at high-redshifts, which is an unavoidable observational contamination in general relativistic cosmology. In particular, we consider the…

Astrophysics · Physics 2009-10-31 Hiromitsu Magira , Y. P. Jing , Yasushi Suto

We study the behavior of a point particle incident from the left on a slab of a randomly diluted triangular array of circular scatterers. Various scattering properties, such as the reflection and transmission probabilities and the…

Statistical Mechanics · Physics 2009-11-07 Hernan Larralde , Francois Leyvraz , Gustavo Martinez-Mekler , Raul Rechtman , Stefano Ruffo

We consider interference effects between p-wave resonance scattering amplitude and background s-wave amplitude in low-energy neutron scattering from a heavy nucleus which goes through the compound nucleus stage. The first effect is in the…

Nuclear Theory · Physics 2009-08-18 J. C. Berengut , V. V. Flambaum , G. F. Gribakin

We use a suite of 3D simulations of star-forming molecular clouds, with and without stellar feedback and magnetic fields, to investigate the effectiveness of different fitting methods for volume and column density probability distribution…

Astrophysics of Galaxies · Physics 2023-05-22 Avery Kiihne , Sabrina M. Appel , Blakesley Burkhart , Vadim A. Semenov , Christoph Federrath

We use both old and new theoretical developments in QCD dispersion relation constraints on the scalar form factor in the decay K --> \pi l \nu_l to obtain constraints on the strange quark mass. The perturbative QCD side of the calculation…

High Energy Physics - Phenomenology · Physics 2014-11-17 Richard F. Lebed , Karl Schilcher

Let $\alpha_n(\cdot)=P\bigl(X_{n+1}\in\cdot\mid X_1,\ldots,X_n\bigr)$ be the predictive distributions of a sequence $(X_1,X_2,\ldots)$ of $p$-dimensional random vectors. Suppose $$\alpha_n= \mathcal{N} _p (M_n,Q_n)$$ where…

Statistics Theory · Mathematics 2024-09-17 Samuele Garelli , Fabrizio Leisen , Luca Pratelli , Pietro Rigo

Diffusion models, which convert noise into new data instances by learning to reverse a diffusion process, have become a cornerstone in contemporary generative modeling. In this work, we develop non-asymptotic convergence theory for a…

Machine Learning · Computer Science 2024-08-06 Gen Li , Yuting Wei , Yuejie Chi , Yuxin Chen
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