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In this paper we study algorithms to find a Gaussian approximation to a target measure defined on a Hilbert space of functions; the target measure itself is defined via its density with respect to a reference Gaussian measure. We employ the…

Numerical Analysis · Mathematics 2014-08-11 Frank J. Pinski , Gideon Simpson , Andrew M. Stuart , Hendrik Weber

Fix $\nu>0$, denote by $G(\nu/2)$ a Gamma random variable with parameter $\nu/2$ and let $n\geq2$ be a fixed even integer. Consider a sequence $\{F_k\}_{k\geq1}$ of square integrable random variables belonging to the $n$th Wiener chaos of a…

Probability · Mathematics 2009-08-28 Ivan Nourdin , Giovanni Peccati

Solving Bayesian inference problems approximately with variational approaches can provide fast and accurate results. Capturing correlation within the approximation requires an explicit parametrization. This intrinsically limits this…

Machine Learning · Statistics 2020-01-31 Jakob Knollmüller , Torsten A. Enßlin

In the paper [25], written in collaboration with Gesine Reinert, we proved a universality principle for the Gaussian Wiener chaos. In the present work, we aim at providing an original example of application of this principle in the…

Probability · Mathematics 2010-02-08 Ivan Nourdin , Giovanni Peccati

We consider the classification problem of a high-dimensional mixture of two Gaussians with general covariance matrices. Using the replica method from statistical physics, we investigate the asymptotic behavior of a general class of…

Machine Learning · Statistics 2024-10-29 Hanwen Huang , Peng Zeng

Two simple undirected graphs are cospectral if their respective adjacency matrices have the same multiset of eigenvalues. Cospectrality yields an equivalence relation on the family of graphs which is provably weaker than isomorphism. In…

Data Structures and Algorithms · Computer Science 2023-06-21 Gaurav Rattan , Tim Seppelt

The Gromov-Wasserstein (GW) distance enables comparing metric measure spaces based solely on their internal structure, making it invariant to isomorphic transformations. This property is particularly useful for comparing datasets that…

Statistics Theory · Mathematics 2024-10-24 Gabriel Rioux , Ziv Goldfeld , Kengo Kato

A variational principle for gauge theories of gravity is presented, which maintains manifest covariance under the symmetries to which the action is invariant, throughout the calculation of the equations of motion and conservation laws. This…

General Relativity and Quantum Cosmology · Physics 2023-09-27 Michael Hobson , Anthony Lasenby , Will Barker

This paper is devoted to establish an invariance principle where the limit process is a multifractional Gaussian process with a multifractional function which takes its values in $(1/2,1)$. Some properties, such as regularity and local…

Probability · Mathematics 2009-09-29 Serge Cohen , Renaud Marty

The main purpose of this paper is to construct convergent series for the approximate calculation of certain integrals over the Gaussian measure with a nuclear covariance operator, nonlocal propagator, in separable Hilbert space. Such series…

High Energy Physics - Theory · Physics 2024-08-06 Nikita A. Ignatyuk , Anna A. Ogarkova , Stanislav L. Ogarkov

We employ stabilization methods and second order Poincar\'e inequalities to establish rates of multivariate normal convergence for a large class of vectors $(H_s^{(1)},...,H_s^{(m)})$, $s \geq 1$, of statistics of marked Poisson processes…

Probability · Mathematics 2021-03-02 Matthias Schulte , J. E. Yukich

We consider Gaussian measures $\mu, \tilde{\mu}$ on a separable Hilbert space, with fractional-order covariance operators $A^{-2\beta}$ resp. $\tilde{A}^{-2\tilde{\beta}}$, and derive necessary and sufficient conditions on $A, \tilde{A}$…

Probability · Mathematics 2023-07-19 David Bolin , Kristin Kirchner

This paper investigates the approximation of Gaussian random variables in Banach spaces, focusing on the high-probability bounds for the approximation of Gaussian random variables using finitely many observations. We derive non-asymptotic…

Statistics Theory · Mathematics 2025-08-28 Daniel Winkle , Ingo Steinwart , Bernard Haasdonk

Galilean invariance is usually violated in self-consistent mean-field calculations that employ effective density-dependent nuclear forces. We present a novel approach, based on variational quantum Monte Carlo techniques, suitable to…

Nuclear Theory · Physics 2020-01-30 P. Massella , F. Barranco , D. Lonardoni , A. Lovato , F. Pederiva , E. Vigezzi

Whittle-Mat\'ern fields are a recently introduced class of Gaussian processes on metric graphs, which are specified as solutions to a fractional-order stochastic differential equation. Unlike earlier covariance-based approaches for…

Methodology · Statistics 2023-10-26 David Bolin , Alexandre Simas , Jonas Wallin

We study sample covariance matrices arising from rectangular random matrices with i.i.d. columns. It was previously known that the resolvent of these matrices admits a deterministic equivalent when the spectral parameter stays bounded away…

Probability · Mathematics 2022-11-24 Clément Chouard

Motivated by problems in controlled experiments, we study the discrepancy of random matrices with continuous entries where the number of columns $n$ is much larger than the number of rows $m$. Our first result shows that if $\omega(1) = m =…

Discrete Mathematics · Computer Science 2020-11-10 Paxton Turner , Raghu Meka , Philippe Rigollet

We derive the 1-loop effective action of the cubic Galileon coupled to quantum-gravitational fluctuations in a background and gauge-independent manner, employing the covariant framework of DeWitt and Vilkovisky. Although the bare action…

High Energy Physics - Theory · Physics 2017-05-24 Ippocratis D. Saltas , Vincenzo Vitagliano

The eigenvalue density for members of the Gaussian orthogonal and unitary ensembles follows the Wigner semi-circle law. If the Gaussian entries are all shifted by a constant amount c/Sqrt(2N), where N is the size of the matrix, in the large…

Mathematical Physics · Physics 2009-04-21 Kevin E. Bassler , Peter J. Forrester , Norman E. Frankel

This article considers linear processes with values in a separable Hilbert space exhibiting long-range dependence. The scaling limits for the sample autocovariance operators at different time lags are investigated in the topology of their…

Probability · Mathematics 2025-06-23 Marie-Christine Düker , Pavlos Zoubouloglou