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The Papangelou intensities of determinantal (or fermion) point processes are investigated. These exhibit a monotonicity property expressing the repulsive nature of the interaction, and satisfy a bound implying stochastic domination by a…

Probability · Mathematics 2010-03-16 Hans-Otto Georgii , Hyun Jae Yoo

The standard Monte Carlo estimator $\widehat{I}_N^{\mathrm{MC}}$ of $\int fd\omega$ relies on independent samples from $\omega$ and has variance of order $1/N$. Replacing the samples with a determinantal point process (DPP), a repulsive…

Machine Learning · Computer Science 2026-04-22 Guillaume Gautier , Rémi Bardenet , Michal Valko

Given multiple orthogonal polynomials on the real line with respect to a system $\bm{\mu} = (\mu_1,\ldots,\mu_r)$, we investigate multiple orthogonal polynomials associated with any rational perturbation of the form $$…

Classical Analysis and ODEs · Mathematics 2026-03-24 Rostyslav Kozhan , Marcus Vaktnäs

We consider Fredholm determinants of the form identity minus product of spectral projections corresponding to isolated parts of the spectrum of a pair of self-adjoint operators. We show an identity relating such determinants to an integral…

Spectral Theory · Mathematics 2018-08-06 Martin Gebert

The Feinberg-Piunovskiy Theorem established in [14, Theorem 3.8] asserts that for a discrete-time uniformly absorbing and atomless Markov Decision Process (MDP) with Borel state space and multiple criteria, the family of deterministic…

Probability · Mathematics 2025-10-07 Francois Dufour , Tomas Prieto-Rumeau

We obtain explicit double-contour representations for the correlation kernels of the discrete orthogonal ($\beta=1$) and symplectic ($\beta=4$) random matrix ensembles with Meixner, Charlier, and Krawtchouk weights. A single…

Mathematical Physics · Physics 2025-11-12 Miguel Tierz

We consider determinantal point processes on the $d$-dimensional unit sphere $\mathbb S^d$. These are finite point processes exhibiting repulsiveness and with moment properties determined by a certain determinant whose entries are specified…

Methodology · Statistics 2016-07-14 Jesper Møller , Morten Nielsen , Emilio Porcu , Ege Rubak

This paper investigates the information geometrical structure of a determinantal point process (DPP). It demonstrates that a DPP is embedded in the exponential family of log-linear models. The extent of deviation from an exponential family…

Statistics Theory · Mathematics 2024-04-18 Hideitsu Hino , Keisuke Yano

Determinantal point processes (DPPs) are repulsive point processes where the interaction between points depends on the determinant of a positive-semi definite matrix. The contributions of this paper are two-fold. First of all, we introduce…

Probability · Mathematics 2022-06-01 Simon Barthelmé , Nicolas Tremblay , Konstantin Usevich , Pierre-Olivier Amblard

We study Fredholm determinants related to a family of kernels which describe the edge eigenvalue behavior in unitary random matrix models with critical edge points. The kernels are natural higher order analogues of the Airy kernel and are…

Mathematical Physics · Physics 2009-01-19 T. Claeys , A. Its , I. Krasovsky

It is well known that the independence of the sample mean and the sample variance characterizes the normal distribution. By using Anosov's theorem, we further investigate the analogous characteristic properties in terms of the sample mean…

Statistics Theory · Mathematics 2021-12-14 Chin-Yuan Hu , Gwo Dong Lin

With view to applications in stochastic analysis and geometry, we introduce a new correspondence for positive definite kernels (p.d.) $K$ and their associated reproducing kernel Hilbert spaces. With this we establish two kinds of…

Functional Analysis · Mathematics 2019-11-28 Palle Jorgensen , Feng Tian

It is known that determinantal point processes have an intimate relation to operator algebras. In the paper, we extend this relationship to encompass dynamical aspects. Especially, we delve into two types of determinantal point processes.…

Operator Algebras · Mathematics 2023-09-06 Ryosuke Sato

Behavioural metrics have been shown to be an effective mechanism for constructing representations in reinforcement learning. We present a novel perspective on behavioural metrics for Markov decision processes via the use of positive…

Machine Learning · Computer Science 2023-11-01 Pablo Samuel Castro , Tyler Kastner , Prakash Panangaden , Mark Rowland

Determinantal point processes (DPPs) are probabilistic models for repulsion. When used to represent the occurrence of random subsets of a finite base set, DPPs allow to model global negative associations in a mathematically elegant and…

Statistics Theory · Mathematics 2019-01-29 Kayvan Sadeghi , Alessandro Rinaldo

We continue the investigation of the spectral theory and exponential asymptotics of Markov processes, following Kontoyiannis and Meyn (2003). We introduce a new family of nonlinear Lyapunov drift criteria, characterizing distinct subclasses…

Probability · Mathematics 2007-05-23 Ioannis Kontoyiannis , S. P. Meyn

We introduce a priori Sobolev-space error estimates for the solution of nonlinear, and possibly parametric, PDEs using Gaussian process and kernel based methods. The primary assumptions are: (1) a continuous embedding of the reproducing…

Numerical Analysis · Mathematics 2023-05-10 Pau Batlle , Yifan Chen , Bamdad Hosseini , Houman Owhadi , Andrew M Stuart

In 1982 Macdonald published his now famous constant term conjectures for classical root systems. This paper begins with the almost trivial observation that Macdonald's constant term identities admit an extra set of free parameters, thereby…

Combinatorics · Mathematics 2015-09-08 Gyula Karolyi , Alain Lascoux , S. Ole Warnaar

Determinantal point processes (DPPs) are repulsive point processes where the interaction between points depends on the determinant of a positive-semi definite matrix. In this paper, we study the limiting process of L-ensembles based on…

Probability · Mathematics 2022-06-01 Simon Barthelmé , Nicolas Tremblay , Konstantin Usevich , Pierre-Olivier Amblard

In this article we use techniques of proof mining to analyse a result, due to Yonghong Yao and Muhammad Aslam Noor, concerning the strong convergence of a generalized proximal point algorithm which involves multiple parameters. Yao and…

Logic · Mathematics 2021-01-13 Bruno Dinis , Pedro Pinto