English
Related papers

Related papers: Exact statistics of complex zeros for Gaussian ran…

200 papers

Gaussian processes (GPs) are flexible non-parametric models, with a capacity that grows with the available data. However, computational constraints with standard inference procedures have limited exact GPs to problems with fewer than about…

We compute the spectral correlation functions for the transition from a harmonic oscillator towards the Gaussian Unitary Ensemble (GUE). We use a variant of the supersymmetry method to obtain analytical results in a fast and elegant way. In…

chao-dyn · Physics 2009-10-31 Thomas Guhr , Thomas Papenbrock

Products and sums of random matrices have seen a rapid development in the past decade due to various analytical techniques available. Two of these are the harmonic analysis approach and the concept of polynomial ensembles. Very recently, it…

Probability · Mathematics 2023-02-02 Mario Kieburg

We construct a N-dimensional Gaussian landscape with multiscale, translation invariant, logarithmic correlations and investigate the statistical mechanics of a single particle in this environment. In the limit of high dimension N>>1 the…

Disordered Systems and Neural Networks · Physics 2009-11-13 Yan V Fyodorov , Jean-Philippe Bouchaud

The large degree asymptotics of the expected number of real zeros of a random trigonometric polynomial $$ T_n(x) = \sum_ {j=0} ^{n} a_j \cos (j x) + b_j \sin (j x), \ x \in (0,2\pi), $$ with i.i.d. real-valued standard Gaussian coefficients…

Probability · Mathematics 2021-11-01 Ali Pirhadi

We investigate the pairwise negative correlation (p-NC) property for uniform probability measures on several families of spanning subgraphs of the complete graph $K_n$. Motivated by conjectured negative dependence properties of the…

Probability · Mathematics 2026-03-12 Pengfei Tang , Zibo Zhang

Following a systematic analysis of existing results, we investigate when complete interlacing between the zeros of distinct polynomial sequences, $\{\mathcal{P}_n\}$ and $\{\mathcal{G}_n\}$ can be achieved by using a naturally arising extra…

Classical Analysis and ODEs · Mathematics 2026-04-07 Kerstin Jordaan , Vikash Kumar

Kontsevitch's work on Airy matrix integrals has led to explicit results for the intersection numbers of the moduli space of curves. In a subsequent work Okounkov rederived these results from the edge behavior of a Gaussian matrix integral.…

Mathematical Physics · Physics 2009-11-13 E. Brezin , S. Hikami

We obtain some results on the asymptotic behaviour of Geometric polynomials in both the complex plane minus $[-1,0]$ and the interval $(-1,0)$. We also find the distance of consecutive zeros of these polynomials in the bulk of the interval…

Classical Analysis and ODEs · Mathematics 2026-04-30 M. Bello-Hernández , M. Benito , Ó. Ciaurri , E. Fernández

Consider random polynomials of the form $G_n = \sum_{i=0}^n \xi_i p_i$, where the $\xi_i$ are i.i.d.\ non-degenerate complex random variables, and $\{p_i\}$ is a sequence of orthonormal polynomials with respect to a regular measure $\tau$…

Probability · Mathematics 2021-10-29 Duncan Dauvergne

The k-point correlation functions of the Gaussian Random Matrix Ensembles are certain determinants of functions which depend on only two arguments. They are referred to as kernels, since they are the building blocks of all correlations. We…

Mathematical Physics · Physics 2009-11-10 Johan Groenqvist , Thomas Guhr , Heiner Kohler

Supersymmetric vacua (`universes') of string/M theory may be identified with certain critical points of a holomorphic section (the `superpotential') of a Hermitian holomorphic line bundle over a complex manifold. An important physical…

Complex Variables · Mathematics 2009-11-10 Michael R. Douglas , Bernard Shiffman , Steve Zelditch

We study principal components regression (PCR) in an asymptotic high-dimensional regression setting, where the number of data points is proportional to the dimension. We derive exact limiting formulas for the estimation and prediction…

Statistics Theory · Mathematics 2025-09-18 Alden Green , Elad Romanov

We consider a class of Gaussian random holomorphic functions, whose expected zero set is uniformly distributed over $\C^n $. This class is unique (up to multiplication by a non zero holomorphic function), and is closely related to a…

Complex Variables · Mathematics 2007-05-23 Scott Zrebiec

Generalized Polynomial Chaos (gPC) expansions are well established for forward uncertainty propagation in many application areas. Although the associated computational effort may be reduced in comparison to Monte Carlo techniques, for…

Computational Engineering, Finance, and Science · Computer Science 2023-07-26 Niklas Georg , Ulrich Römer

We discuss the correspondence between Gaussian process regression and Geometric Harmonics, two similar kernel-based methods that are typically used in different contexts. Research communities surrounding the two concepts often pursue…

Machine Learning · Statistics 2021-10-07 Felix Dietrich , Juan M. Bello-Rivas , Ioannis G. Kevrekidis

We compute the exact asymptotics for the cumulants of linear statistics associated with the zeros counting measure of a large class of real Gaussian processes. Precisely, we show that if the underlying covariance function is regular and…

Probability · Mathematics 2023-10-09 Louis Gass

The main results of this article are asymptotic formulas for the variance of the number of zeros of a Gaussian random polynomial of degree $N$ in an open set $U \subset C$ as the degree $N \to \infty$, and more generally for the zeros of…

Complex Variables · Mathematics 2007-05-23 Bernard Shiffman , Steve Zelditch

We review the Exact Renormalization Group equations of Wegner and Houghton in an approximation which permits both numerical and analytical studies of nonperturbative renormalization flows. We obtain critical exponents numerically and with…

High Energy Physics - Theory · Physics 2009-09-25 Peter E. Haagensen , Yuri Kubyshin , José Ignacio Latorre , E. Moreno

Statistical physics approaches can be used to derive accurate predictions for the performance of inference methods learning from potentially noisy data, as quantified by the learning curve defined as the average error versus number of…

Machine Learning · Statistics 2012-11-07 Matthew J. Urry , Peter Sollich
‹ Prev 1 3 4 5 6 7 10 Next ›