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We use discrete analogs of Riemann-Hilbert problem's methods to derive the discrete Bessel kernel which describes the poissonized Plancherel measures for symmetric groups. To do this we define discrete analogs of a Riemann-Hilbert problem…

Combinatorics · Mathematics 2007-05-23 Alexei Borodin

The authors use Riemann-Hilbert methods to compute the constant that arises in the asymptotic behavior of the Airy-kernel determinant of random matrix theory.

Functional Analysis · Mathematics 2007-06-21 P. Deift , A. Its , I. Krasovsky

We compute the full off-diagonal asymptotics of the equivariant and partial Bergman kernels associated with a circle action on a prequantized K\"ahler manifold with bounded geometry at infinity, then use these results to compute the…

Differential Geometry · Mathematics 2025-11-26 Louis Ioos

We study the hermitian one matrix model with semi-classical potential. This is a general unitary invariant random matrix ensemble in which the potential has a derivative that is a rational function and the measure is supported on some…

Mathematical Physics · Physics 2015-04-20 Max R. Atkin

We establish the asymptotic normality of the kernel type estimator for the regression function constructed from quasi-associated data when the explanatory variable takes its values in a separable Hilbert space.

Statistics Theory · Mathematics 2018-05-08 Lahcen Douge

We derive the leading asymptotic behavior and build a new series representation for the Fredholm determinant of integrable integral operators appearing in the representation of the time and distance dependent correlation functions of…

Mathematical Physics · Physics 2015-04-30 K. K. Kozlowski

In this paper we investigate the problem of estimating the regression function in models with correlated observations. The data is obtained from several experimental units each of them forms a time series. We propose a new estimator based…

Statistics Theory · Mathematics 2019-06-13 Djihad Benelmadani , Karim Benhenni , Sana Louhichi

The matrix Whittaker kernel has been introduced by A. Borodin in Part IV of the present series of papers. This kernel describes a point process -- a probability measure on a space of countable point configurations. The kernel is expressed…

Representation Theory · Mathematics 2007-05-23 Grigori Olshanski

We consider the determinantal point process with the confluent hypergeometric kernel. This process is a universal point process in random matrix theory and describes the distribution of eigenvalues of large random Hermitian matrices near…

Mathematical Physics · Physics 2024-02-20 Shuai-Xia Xu , Shu-Quan Zhao , Yu-Qiu Zhao

In Random Matrix Theory the local correlations of the Laguerre and Jacobi Unitary Ensemble in the hard edge scaling limit can be described in terms of the Bessel kernel (containing a parameter $\alpha$). In particular, the so-called hard…

Functional Analysis · Mathematics 2010-01-15 Torsten Ehrhardt

Many scientific problems involve data exhibiting both temporal and cross-sectional dependencies. While linear dependencies have been extensively studied, the theoretical analysis of regression estimators under nonlinear dependencies remains…

Statistics Theory · Mathematics 2025-02-27 Marie-Christine Düker , Adam Waterbury

We investigate the asymptotic behavior of a generalized sine kernel acting on a finite size interval [-q,q]. We determine its asymptotic resolvent as well as the first terms in the asymptotic expansion of its Fredholm determinant. Further,…

Mathematical Physics · Physics 2011-10-07 N. Kitanine , Karol K. Kozlowski , Jean Michel Maillet , N. A. Slavnov , Véronique Terras

In this paper we provide a finite-sample and an infinite-sample representer theorem for the concatenation of (linear combinations of) kernel functions of reproducing kernel Hilbert spaces. These results serve as mathematical foundation for…

Machine Learning · Computer Science 2018-06-08 Bastian Bohn , Michael Griebel , Christian Rieger

We describe a method to perform functional operations on probability distributions of random variables. The method uses reproducing kernel Hilbert space representations of probability distributions, and it is applicable to all operations…

Machine Learning · Statistics 2016-09-14 Bernhard Schölkopf , Krikamol Muandet , Kenji Fukumizu , Jonas Peters

We develop a coordinate-free probabilistic framework for determinantal point processes associated with Bergman kernels on compact complex manifolds. The basic issue is that Bergman kernels are naturally line-bundle-valued:…

Complex Variables · Mathematics 2026-05-27 Thibaut Lemoine

We give a summary of the results from Parts I-V (math.RT/9804086, math.RT/9804087, math.RT/9804088, math.RT/9810013, math.RT/9810014). Our work originated from harmonic analysis on the infinite symmetric group. The problem of spectral…

Representation Theory · Mathematics 2007-05-23 Alexei Borodin , Grigori Olshanski

We give sharp asymptotic estimates at infinity of all radial partial derivatives of the heat kernel on H-type groups. As an application, we give a new proof of the discreteness of the spectrum of some natural sub-Riemannian…

Classical Analysis and ODEs · Mathematics 2022-05-11 Tommaso Bruno , Mattia Calzi

We study a 2-parametric family of probability measures on the space of countable point configurations on the punctured real line (the points of the random configuration are concentrated near zero). These measures (or, equivalently, point…

Representation Theory · Mathematics 2007-05-23 Alexei Borodin

We prove that general correlation functions of both ratios and products of characteristic polynomials of Hermitian random matrices are governed by integrable kernels of three different types: a) those constructed from orthogonal…

Mathematical Physics · Physics 2009-11-07 Eugene Strahov , Yan V. Fyodorov

We obtain "large gap" asymptotics for a Fredholm determinant with a confluent hypergeometric kernel. We also obtain asymptotics for determinants with two types of Bessel kernels which appeared in random matrix theory.

Mathematical Physics · Physics 2010-10-28 P. Deift , I. Krasovsky , J. Vasilevska
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