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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

We study determinantal point processes (DPP) through the lens of algebraic statistics. We count the critical points of the log-likelihood function, and we compute them for small models, thereby disproving a conjecture of Brunel, Moitra,…

Statistics Theory · Mathematics 2024-01-17 Hannah Friedman , Bernd Sturmfels , Maksym Zubkov

We construct the equilibrium Glauber and Kawasaki dynamics on discrete spaces which leave invariant certain determinantal point processes. We will construct Fellerian Markov processes with specified core for the generators. Further, we…

Mathematical Physics · Physics 2010-01-12 Myeongju Chae , Hyun Jae Yoo

A point process is said to be rigid if for any bounded domain in the phase space, the number of particles in the domain is almost surely determined by the restriction of the configuration to the complement of our bounded domain. The main…

Probability · Mathematics 2015-06-26 Alexander I. Bufetov

We analyze fermionic modes as fundamental entities for quantum information processing. To this end we construct a density operator formalism on the underlying Fock space and demonstrate how it can be naturally and unambiguously equipped…

Quantum Physics · Physics 2013-02-26 Nicolai Friis , Antony R. Lee , David Edward Bruschi

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

The Macdonald process is a stochastic process on the collection of partitions that is a $(q,t)$-deformed generalization of the Schur process. In this paper, we approach the Macdonald process identifying the space of symmetric functions with…

Quantum Algebra · Mathematics 2020-06-19 Shinji Koshida

The purpose of this paper is to compute the asymptotics of determinants of finite sections of operators that are trace class perturbations of Toeplitz operators. For example, we consider the asymptotics in the case where the matrices are of…

Functional Analysis · Mathematics 2008-07-09 Estelle L. Basor , Torsten Ehrhardt

We study integral operators on the space of square-integrable functions from a compact set, $X$, to a separable Hilbert space, $H$. The kernel of such an operator takes values in the ideal of Hilbert-Schmidt operators on $H$. We establish…

Functional Analysis · Mathematics 2024-08-12 John Zweck , Yuri Latushkin , Erika Gallo

In this paper, we introduce a two-parameters determinantal point process in the Poincar\'e disc and compute the asymptotics of the variance of its number of particles inside a disc centered at the origin and of radius $r$ as $r$ tends to…

Probability · Mathematics 2017-12-14 Nizar Demni , Pierre Lazag

We consider a kernel based harmonic analysis of "boundary," and boundary representations. Our setting is general: certain classes of positive definite kernels. Our theorems extend (and are motivated by) results and notions from classical…

Functional Analysis · Mathematics 2016-11-15 Palle Jorgensen , Feng Tian

This paper reviews developments in statistics for spatial point processes obtained within roughly the last decade. These developments include new classes of spatial point process models such as determinantal point processes, models…

Methodology · Statistics 2016-09-06 Jesper Møller , Rasmus Waagepetersen

In this paper, we study the full asymptotic expansion of the partition functions of determinantal point processes defined on a polarized K\"ahler manifold. We show that the coefficients of the expansion are given by geometric functionals on…

Differential Geometry · Mathematics 2026-01-01 Kiyoon Eum

Let $X$ be a locally compact Polish space and $\sigma$ a nonatomic reference measure on $X$ (typically $X=\mathbb R^d$ and $\sigma$ is the Lebesgue measure). Let $X^2\ni(x,y)\mapsto\mathbb K(x,y)\in\mathbb C^{2\times 2}$ be a $2\times…

Probability · Mathematics 2022-08-24 Maryam Gharamah Ali Alshehri , Eugene Lytvynov

We consider the Laplace-Beltrami operator $\Delta_g$ on a smooth, compact Riemannian manifold $(M,g)$ and the determinantal point process $\mathcal{X}_{\lambda}$ on $M$ associated with the spectral projection of $-\Delta_g$ onto the…

Probability · Mathematics 2022-03-16 Makoto Katori , Tomoyuki Shirai

We present randomized algorithms for estimating the trace and deter- minant of Hermitian positive semi-definite matrices. The algorithms are based on subspace iteration, and access the matrix only through matrix vector products. We analyse…

Numerical Analysis · Mathematics 2017-02-17 Arvind K. Saibaba , Alen Alexanderian , Ilse C. F. Ipsen

We show that the symplectic and orthogonal character analogues of Okounkov's Schur measure (on integer partitions) are determinantal, with explicit correlation kernels. We apply this to prove certain Borodin-Okounkov-Gessel-type results…

Probability · Mathematics 2020-01-31 Dan Betea

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

The accuracy and effectiveness of Hermite spectral methods for the numerical discretization of partial differential equations on unbounded domains, are strongly affected by the amplitude of the Gaussian weight function employed to describe…

Numerical Analysis · Mathematics 2021-04-07 Lorella Fatone , Daniele Funaro , Gianmarco Manzini

Determinantal point processes (DPPs) are well known models for diverse subset selection problems, including recommendation tasks, document summarization and image search. In this paper, we discuss a greedy deterministic adaptation of k-DPP.…

Machine Learning · Computer Science 2021-05-31 Joachim Schreurs , Michaël Fanuel , Johan A. K. Suykens
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