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Concerning systematic effects, the recommendation given in the GUM is to correct for them, but unfortunately no detailed information is available, how to do this. This publication will show, how systematic measurement deviations can be…

Data Analysis, Statistics and Probability · Physics 2011-02-16 Michael Krystek

A new class of nonparametric prior distributions, termed Beta-Binomial stick-breaking process, is proposed. By allowing the underlying length random variables to be dependent through a Beta marginals Markov chain, an appealing discrete…

Statistics Theory · Mathematics 2020-08-12 María F. Gil-Leyva , Ramsés H. Mena , Theodoros Nicoleris

For d nonpolar compact sets K_1,...,K_d in the complex plane, d admissible weights Q_1,...,Q_d, and a positive semidefinite d x d interaction matrix C with no zero column, we define natural discretizations of the associated weighted vector…

Complex Variables · Mathematics 2013-01-08 T. Bloom , N. Levenberg , F. Wielonsky

Measures of discordance between datasets have become an essential part of cosmological analyses. It is important to accurately evaluate the significance of such discordances when present. We propose here a Bayesian interpretation of…

Cosmology and Nongalactic Astrophysics · Physics 2021-05-12 Weikang Lin , Mustapha Ishak

Minimum divergence estimators provide a natural choice of estimators in a statistical inference problem. Different properties of various families of these divergence measures such as Hellinger distance, power divergence, density power…

Statistics Theory · Mathematics 2025-07-08 Subhrajyoty Roy , Supratik Basu , Abhik Ghosh , Ayanendranath Basu

Bound-to-Bound Data Collaboration (B2BDC) provides a natural framework for addressing both forward and inverse uncertainty quantification problems. In this approach, QOI (quantity of interest) models are constrained by related experimental…

Optimization and Control · Mathematics 2019-04-02 Arun Hegde , Wenyu Li , James Oreluk , Andrew Packard , Michael Frenklach

We study the dimension properties of the spectral measure of the Circular $\beta$-Ensembles. For $\beta \geq 2$ it it was previously shown by Simon that the spectral measure is almost surely singular continuous with respect to Lebesgue…

Probability · Mathematics 2020-03-25 Tom Alberts , Raoul Normand

The Airy$_\beta$ line ensemble is a random collection of continuous curves, which should serve as a universal edge scaling limit in problems related to eigenvalues of random matrices and models of 2d statistical mechanics. This line…

Probability · Mathematics 2024-11-19 Vadim Gorin , Jiaming Xu , Lingfu Zhang

We study the large deviations behavior of systems that admit a certain form of a product distribution, which is frequently encountered both in Physics and in various information system models. First, to fix ideas, we demonstrate a simple…

Statistical Mechanics · Physics 2015-05-14 Neri Merhav , Yariv Kafri

Objective Bayesian inference procedures are derived for the parameters of the multivariate random effects model generalized to elliptically contoured distributions. The posterior for the overall mean vector and the between-study covariance…

Methodology · Statistics 2021-04-07 Olha Bodnar , Taras Bodnar

We study the size of the largest biconnected components in sparse Erd\H{o}s-R\'enyi graphs with finite connectivity and Barab\'asi-Albert graphs with non-integer mean degree. Using a statistical-mechanics inspired Monte Carlo approach we…

Disordered Systems and Neural Networks · Physics 2019-04-05 Hendrik Schawe , Alexander K. Hartmann

We consider the problem of bounding large deviations for non-i.i.d. random variables that are allowed to have arbitrary dependencies. Previous works typically assumed a specific dependence structure, namely the existence of independent…

Probability · Mathematics 2018-11-06 Christoph H. Lampert , Liva Ralaivola , Alexander Zimin

We prove a large deviation principle for a sequence of point processes defined by Gibbs probability measures on a Polish space. This is obtained as a consequence of a more general Laplace principle for the non-normalized Gibbs measures. We…

Probability · Mathematics 2020-04-08 David García-Zelada

We prove Burnett's conjecture in general relativity when the metrics satisfy a generalized wave coordinate condition, i.e., suppose $\{g_n\}_{n=1}^\infty$ is a sequence of Lorentzian metrics (in arbitrary dimensions $d \geq 3$) satisfying a…

General Relativity and Quantum Cosmology · Physics 2024-03-07 Cécile Huneau , Jonathan Luk

We provide a Lyapunov type bound in the multivariate central limit theorem for sums of independent, but not necessarily identically distributed random vectors. The error in the normal approximation is estimated for certain classes of sets,…

Probability · Mathematics 2019-07-24 Martin Raič

We extend recent results on the Asymptotic Equipartition Property for the density of $n$ particles in $\beta$-ensembles, as $n$ tends to infinity. We prove the Large Deviation Principle of the log-density for a general potential and the…

Probability · Mathematics 2018-03-14 Martina Dal Borgo , Emma Hovhannisyan , Alain Rouault

This paper explores certain kinds of empirical process with respect to the components of multivariate Gaussian. We put forward some finite sample bounds which hold for multivariate Gaussian under general dependence. We give necessary and…

Probability · Mathematics 2020-07-03 Jikai Hou

We study the many-body dynamics of an initially factorized bosonic wave function in the mean-field regime. We prove large deviation estimates for the fluctuations around the condensate. We derive an upper bound extending a recent result to…

Mathematical Physics · Physics 2022-07-07 Simone Rademacher , Robert Seiringer

In this paper we study empirical measures which can be thought as a decoupled version of the empirical measures generated by random matrices. We prove the large deviation principle with the rate function, which is finite only on product…

Probability · Mathematics 2007-05-23 Wlodek Bryc

We study two-layer belief networks of binary random variables in which the conditional probabilities Pr[childlparents] depend monotonically on weighted sums of the parents. In large networks where exact probabilistic inference is…

Machine Learning · Computer Science 2013-02-01 Michael Kearns , Lawrence Saul