English
Related papers

Related papers: Discrepancy of determinantal point processes on co…

200 papers

We consider the behavior of spatial point processes when subjected to a class of linear transformations indexed by a variable T. It was shown in Ellis [Adv. in Appl. Probab. 18 (1986) 646-659] that, under mild assumptions, the transformed…

Probability · Mathematics 2007-05-23 Dominic Schuhmacher

We study the $L_p$-discrepancy of random point sets in high dimensions, with emphasis on small values of $p$. Although the classical $L_p$-discrepancy suffers from the curse of dimensionality for all $p \in (1,\infty)$, the gap between…

Numerical Analysis · Mathematics 2025-12-10 Erich Novak , Friedrich Pillichshammer

It is well known that one can map certain properties of random matrices, fermionic gases, and zeros of the Riemann zeta function to a unique point process on the real line. Here we analytically provide exact generalizations of such a point…

Statistical Mechanics · Physics 2009-11-13 Salvatore Torquato , A. Scardicchio , Chase E Zachary

For the plane, sphere, and hyperbolic plane we consider the canonical invariant determinantal point processes with intensity rho dnu, where nu is the corresponding invariant measure. We show that as rho converges to infinity, after…

Probability · Mathematics 2021-03-23 Brian Rider , Balint Virag

Let $C\subset\mathbb{R}^2$ be a convex body, and for a positive integer $N$, let $\mathcal{P}$ be a configuration of $N$ points in $[0,1)^2$. The discrepancy of $\mathcal{P}$ with respect to $C$ is defined by \begin{equation*}…

Classical Analysis and ODEs · Mathematics 2026-03-05 Thomas Beretti

We derive upper bounds on the difference between the orthogonal projections of a smooth function $u$ onto two finite element spaces that are nearby, in the sense that the support of every shape function belonging to one but not both of the…

Numerical Analysis · Mathematics 2014-08-19 Evan S. Gawlik , Adrian J. Lew

We study the asymptotics of the expected Wasserstein distance between the empirical measure of a Point Process and the background volume form. The main DPP studied is the harmonic ensemble, where we get the optimal rate of convergence for…

Probability · Mathematics 2026-04-20 Pablo García-Arias

We investigate the average characteristic polynomial $\mathbb E\big[\prod_{i=1}^N(z-x_i)\big] $ where the $x_i$'s are real random variables which form a determinantal point process associated to a bounded projection operator. For a subclass…

Probability · Mathematics 2015-01-08 Adrien Hardy

In this paper we propose an acceptance-rejection sampler using stratified inputs as diver sequence. We estimate the discrepancy of the points generated by this algorithm. First we show an upper bound on the star discrepancy of order…

Computation · Statistics 2014-08-11 Houying Zhu , Josef Dick

Motivated by the Beck-Fiala conjecture, we study the discrepancy problem in two related models of random hypergraphs on $n$ vertices and $m$ edges. In the first (edge-independent) model, a random hypergraph $H_1$ is constructed by fixing a…

Combinatorics · Mathematics 2024-01-12 Calum MacRury , Tomáš Masařík , Leilani Pai , Xavier Pérez-Giménez

This paper establishes mesoscopic rates of convergence in the $L^1$-Wasserstein distance for eigenvalue determinantal point processes (DPPs) derived from the Laguerre Unitary Ensemble (LUE) to the corresponding limiting point process (Airy…

Probability · Mathematics 2026-05-14 Mengchun Cai

Determinantal Point Processes (DPPs) are popular models for point processes with repulsion. They appear in numerous contexts, from physics to graph theory, and display appealing theoretical properties. On the more practical side of things,…

Statistics Theory · Mathematics 2018-08-22 Simon Barthelmé , Pierre-Olivier Amblard , Nicolas Tremblay

Let $X_1,...,X_n$ be a random sample from some unknown probability density $f$ defined on a compact homogeneous manifold $\mathbf M$ of dimension $d \ge 1$. Consider a 'needlet frame' $\{\phi_{j \eta}\}$ describing a localised projection…

Statistics Theory · Mathematics 2012-08-22 Gerard Kerkyacharian , Richard Nickl , Dominique Picard

We prove quantitative homogenization results for harmonic functions on supercritical continuum percolation clusters--that is, Poisson point clouds with edges connecting points which are closer than some fixed distance. We show that, on…

Probability · Mathematics 2025-09-15 Scott Armstrong , Raghavendra Venkatraman

A determinantal point process (DPP) is an ensemble of random nonnegative-integer-valued Radon measures, whose correlation functions are all given by determinants specified by an integral kernel called the correlation kernel. First we show…

Probability · Mathematics 2020-03-11 Makoto Katori

This paper studies quantitative deviation bounds for statistical ensembles evolving under the one-parameter flow of a nearly integrable Hamiltonian system. Combining Nekhoroshev-type stability estimates with phase-mixing arguments, we…

Dynamical Systems · Mathematics 2026-02-23 Xinyu Liu , Yong Li

This paper introduces a new modeling framework for the statistical analysis of point patterns on a manifold M_{d}, defined by a connected and compact two-point homogeneous space, including the special case of the sphere. The presented…

Methodology · Statistics 2022-10-21 M. P. Frías , A. Torres , M. D. Ruiz-Medina

Let $X(D,1) =\Gamma(D,1) \backslash \mathbb{H}$ denote the Shimura curve of level $N=1$ arising from an indefinite quaternion algebra of fixed discriminant $D$. We study the discrete average of the error term in the hyperbolic circle…

Number Theory · Mathematics 2020-01-16 Montserrat Alsina , Dimitrios Chatzakos

In this note, we show that determinantal point processes on the real line corresponding to de Branges spaces of entire functions are rigid in the sense of Ghosh-Peres and, under certain additional assumptions, quasi-invariant under the…

Probability · Mathematics 2016-06-07 Alexander I. Bufetov , Tomoyuki Shirai

Diffusion probabilistic models generate samples by learning to reverse a noise-injection process that transforms data into noise. A key development is the reformulation of the reverse sampling process as a deterministic probability flow…

Machine Learning · Computer Science 2025-08-15 Daniel Zhengyu Huang , Jiaoyang Huang , Zhengjiang Lin