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Related papers: Finite range Decomposition of Gaussian Processes

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We prove the existence as well as regularity of a finite range decomposition for the resolvent $G_{\alpha} (x-y,m^2) = ((-\Delta)^{\alpha\over 2} + m^{2})^{-1} (x-y) $, for $0<\alpha<2$ and all real $m$, in the lattice ${\mathbf Z}^{d}$ as…

Mathematical Physics · Physics 2016-12-12 P. K. Mitter

We consider a family of gradient Gaussian vector fields on $\Z^d$, where the covariance operator is not translation invariant. A uniform finite range decomposition of the corresponding covariance operators is proven, i.e., the covariance…

Mathematical Physics · Physics 2015-10-27 Eris Runa

Let a family of gradient Gaussian vector fields on $ \mathbb{Z}^d $ be given. We show the existence of a uniform finite range decomposition of the corresponding covariance operators, that is, the covariance operator can be written as a sum…

Mathematical Physics · Physics 2012-02-07 Stefan Adams , Roman Kotecký , Stefan Müller

We consider a family of gradient Gaussian vector fields on the torus $(\mathbb{Z}/L^N\mathbb{Z})^d$. Adams, Koteck\'{y}, M\"{u}ller and independently Bauerschmidt established the existence of a uniform finite range decomposition of the…

Mathematical Physics · Physics 2018-11-15 Simon Buchholz

In previous papers, [M1, M2], [M3], we proved the existence as well as regularity of a finite range decomposition for the resolvent $G_{\alpha} (x-y,m^2) = ((-\Delta)^{\alpha\over 2} + m^{2})^{-1} (x-y) $, for $0<\alpha <2$ and all real…

Mathematical Physics · Physics 2017-08-02 P. K. Mitter

Let $G$ be a simply connected, solvable Lie group and $\Gamma$ a lattice in $G$. The deformation space $\mathcal{D}(\Gamma,G)$ is the orbit space associated to the action of $\Aut(G)$ on the space $\mathcal{X}(\Gamma,G)$ of all lattice…

Differential Geometry · Mathematics 2014-02-26 Oliver Baues , Benjamin Klopsch

We introduce a new scalable variational Gaussian process approximation which provides a high fidelity approximation while retaining general applicability. We propose the harmonic kernel decomposition (HKD), which uses Fourier series to…

Machine Learning · Computer Science 2021-06-14 Shengyang Sun , Jiaxin Shi , Andrew Gordon Wilson , Roger Grosse

Recognizing the regime of positive definiteness for a strictly logarithmic covariance kernel, we prove that the small deviations of a related Gaussian multiplicative chaos (GMC) $M_\gamma$ are for each natural dimension $d$ always of…

Probability · Mathematics 2024-06-04 Anna Talarczyk , Maciej Wiśniewolski

In this article we establish novel decompositions of Gaussian fields taking values in suitable spaces of generalized functions, and then use these decompositions to prove results about Gaussian multiplicative chaos. We prove two…

Probability · Mathematics 2019-04-29 Janne Junnila , Eero Saksman , Christian Webb

The noncommutative space $\mathbb{R}^3_\lambda$, a deformation of $\mathbb{R}^3$, supports a $3$-parameter family of gauge theory models with gauge-invariant harmonic term, stable vacuum and which are perturbatively finite to all orders.…

Mathematical Physics · Physics 2016-12-20 Jean-Christophe Wallet

The canonical partition function of a system of rotators (classical X-Y spins) on a lattice, coupled by terms decaying as the inverse of their distance to the power alpha, is analytically computed. It is also shown how to compute a…

Statistical Mechanics · Physics 2009-10-31 Alessandro Campa , Andrea Giansanti , Daniele Moroni

We review and present some known results for non-linear functionals of Gaussian variables in the context of discrete Gaussian fields defined on the $d$ dimensional lattice. Our main result is a Central Limit Theorem in the spirit of the…

Probability · Mathematics 2025-12-16 Fabio Coppini , Wioletta M. Ruszel

Let $I=(a,b)\times(c,d)\subset {\mathbb R}_{+}^2$ be an index set and let $\{G_{\alpha}(x) \}_{\alpha \in I}$ be a collection of Gaussian functions, i.e. $G_{\alpha}(x) = \exp(-\alpha_1 x_1^2 - \alpha_2 x_2^2)$, where $\alpha = (\alpha_1,…

Classical Analysis and ODEs · Mathematics 2022-06-17 Ilya Zlotnikov

We consider an one-dimensional lattice system of unbounded and continuous spins. The Hamiltonian consists of a perturbed strictly-convex single-site potential and with longe-range interaction. We show that if the interactions decay…

Probability · Mathematics 2015-06-17 Georg Menz , Robin Nittka

In this paper, we investigate a class of approximate Gaussian processes (GP) obtained by taking a linear combination of compactly supported basis functions with the basis coefficients endowed with a dependent Gaussian prior distribution.…

Statistics Theory · Mathematics 2025-06-02 Jaehoan Kim , Anirban Bhattacharya , Debdeep Pati

In a previous paper {GN2} an effective solution of the lattice point counting problem in general domains in semisimple S-algebraic groups and affine symmetric varieties was established. The method relies on the mean ergodic theorem for the…

Number Theory · Mathematics 2019-02-20 Alexander Gorodnik , Amos Nevo

Let $\g$ be a simple complex Lie algebra of type $G_2$, $F_4$, or $E_8$, and let $G$ be the unique connected simply connected Lie group with $\mathrm{Lie}(G)=\g$ with compact real form $K$. We prove a triangular decomposition theorem for…

Quantum Algebra · Mathematics 2026-03-24 Ayan Dey

We study a 3-parametric family of stochastic point processes on the one-dimensional lattice originated from a remarkable family of representations of the infinite symmetric group. We prove that the correlation functions of the processes are…

Representation Theory · Mathematics 2009-10-31 Alexei Borodin , Grigori Olshanski

The present work stemmed from the study of the problem of harmonic analysis on the infinite-dimensional unitary group U(\infty). That problem consisted in the decomposition of a certain 4-parameter family of unitary representations, which…

Representation Theory · Mathematics 2016-03-10 Vadim Gorin , Grigori Olshanski

Large-scale Gaussian process inference has long faced practical challenges due to time and space complexity that is superlinear in dataset size. While sparse variational Gaussian process models are capable of learning from large-scale data,…

Machine Learning · Statistics 2018-01-23 Ching-An Cheng , Byron Boots
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