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For a $d-$regular random model, we assign to vertices $q-$state spins. From this model, we define the \emph{empirical co-operate measure}, which enumerates the number of co-operation between a given couple of spins, and \emph{ empirical…

Probability · Mathematics 2017-11-15 U. Ibrahim , A. Lotsi , K. Doku-Amponsah

We introduce stochastic variational inference for Gaussian process models. This enables the application of Gaussian process (GP) models to data sets containing millions of data points. We show how GPs can be vari- ationally decomposed to…

Machine Learning · Computer Science 2013-09-27 James Hensman , Nicolo Fusi , Neil D. Lawrence

In this paper we derive sharp lower and upper bounds for the covariance of two bounded random variables when knowledge about their expected values, variances or both is available. When only the expected values are known, our result can be…

Probability · Mathematics 2021-06-21 Ola Hössjer , Arvid Sjölander

We present examples of Lax-integrable multi-dimensional systems of partial differential equations with higher local symmetries. We also consider Lagrangian deformations of these equations and construct variational bivectors on them.

Exactly Solvable and Integrable Systems · Physics 2014-12-23 H. Baran , I. S. Krasil'shchik , O. I. Morozov , P. Vojčák

The relation between Pearson's correlation coefficient and Salton's cosine measure is revealed based on the different possible values of the division of the L1-norm and the L2-norm of a vector. These different values yield a sheaf of…

Information Retrieval · Computer Science 2012-07-25 Leo Egghe , Loet Leydesdorff

We derive central limit theorems for the Wasserstein distance between the empirical distributions of Gaussian samples. The cases are distinguished whether the underlying laws are the same or different. Results are based on the (quadratic)…

Statistics Theory · Mathematics 2016-02-22 Thomas Rippl , Axel Munk , Anja Sturm

Motivated by the recent contribution \cite{BB17} we study the scaling limit behavior of a class of one-dimensional stochastic differential equations which has a unique attracting point subject to a small additional repulsive perturbation.…

Mathematical Physics · Physics 2019-06-26 Martin Kolb , Matthias Liesenfeld

We derive exact analytic results for several four-point correlation functions for statistical models exhibiting phase separation in two-dimensions. Our theoretical results are then specialized to the Ising model on the two-dimensional strip…

Statistical Mechanics · Physics 2021-10-27 Alessio Squarcini , Antonio Tinti

We predict differential decay distributions for phi->rho gamma gamma and phi -> omega gamma gamma using chiral perturbation theory. We also consider the isospin violating decay phi -> omega pi^0. Experimental information on these decays can…

High Energy Physics - Phenomenology · Physics 2009-10-28 Adam K. Leibovich , Aneesh V. Manohar , Mark B. Wise

We investigate the first-order correction in the homogenization of linear parabolic equations with random coefficients. In dimension $3$ and higher and for coefficients having a finite range of dependence, we prove a pointwise version of…

Probability · Mathematics 2015-09-17 Yu Gu , Jean-Christophe Mourrat

In this paper, we establish a large deviation principle for the conservative stochastic partial differential equations, whose solutions are related to stochastic differential equations with interaction. The weak convergence method and the…

Probability · Mathematics 2023-07-13 Ping Chen , Tusheng Zhang

In this paper we study Strichartz estimates for dispersive equations which are defined by radially symmetric pseudo-differential operators, and of which initial data belongs to spaces of Sobolev type defined in spherical coordinates. We…

Analysis of PDEs · Mathematics 2012-12-06 Yonggeun Cho , Sanghyuk Lee

We prove optimal pointwise Schauder estimates in the spatial variables for solutions of linear parabolic integro-differential equations. Optimal H\"older estimates in space-time for those spatial derivatives are also obtained.

Analysis of PDEs · Mathematics 2015-06-05 Tianling Jin , Jingang Xiong

Bayesian methods have been widely used in the last two decades to infer statistical properties of spatially variable coefficients in partial differential equations from measurements of the solutions of these equations. Yet, in many cases…

Numerical Analysis · Mathematics 2022-03-01 David Aristoff , Wolfgang Bangerth

We establish the large deviation principle for stochastic differential equations with averaging in the case when all coefficients of the fast component depend on the slow one, including diffusion.

Probability · Mathematics 2013-06-11 Alexander Yu. Veretennikov

We propose a coefficient that measures dependence in paired samples of functions. It has properties similar to the Pearson correlation, but differs in significant ways: (i) it is designed to measure dependence between curves, (ii) it…

Statistics Theory · Mathematics 2025-10-02 Mihyun Kim , Piotr Kokoszka

Using the variational formula for operator product coefficients a method for perturbative calculation of the short-distance expansion of the Spin-Spin correlation function in the two dimensional Ising model is presented. Results of explicit…

High Energy Physics - Theory · Physics 2009-10-22 B. Mikhak , A. M. Zarkesh

In this note we use an approximation scheme to establish large deviations for quasi-periodic Gevrey cocycles. As an application, we obtain continuity in the cocycle for the Lyapunov exponent.

Dynamical Systems · Mathematics 2025-09-29 Matthew Powell

The main results in this paper concern large deviations for families of non-Gaussian processes obtained as suitable perturbations of continuous centered multivariate Gaussian processes which satisfy a large deviation principle. We present…

Probability · Mathematics 2023-07-06 C. Macci , B. Pacchiarotti

In the context of a high-dimensional linear regression model, we propose the use of an empirical correlation-adaptive prior that makes use of information in the observed predictor variable matrix to adaptively address high collinearity,…

Methodology · Statistics 2022-07-04 Chang Liu , Yue Yang , Howard Bondell , Ryan Martin