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Determinantal point processes are characterized by a special structural property of the correlation functions: they are given by minors of a correlation kernel. However, unlike the correlation functions themselves, this kernel is not…

Probability · Mathematics 2022-06-15 Grigori Olshanski

We study in some generality intertwinings between $h$-transforms of Karlin-McGregor semigroups associated with one dimensional diffusion processes and those of their Siegmund duals. We obtain couplings so that the corresponding processes…

Probability · Mathematics 2021-02-16 Theodoros Assiotis , Neil O'Connell , Jon Warren

We introduce a new strategy for compositional neural surrogates for radiation-matter interactions, a key task spanning domains from particle physics through nuclear and space engineering to medical physics. Exploiting the locality and the…

Determinantal process is a dynamical extension of a determinantal point process such that any spatio-temporal correlation function is given by a determinant specified by a single continuous function called the correlation kernel.…

Probability · Mathematics 2013-07-10 Makoto Katori

Determinantal point processes are point processes whose correlation functions are given by determinants of matrices. The entries of these matrices are given by one fixed function of two variables, which is called the kernel of the point…

Classical Analysis and ODEs · Mathematics 2019-06-27 Marco Stevens

The purpose of this article is to develop a theory behind the occurrence of "path-integral" kernels in the study of extended determinantal point processes and non-intersecting line ensembles. Our first result shows how determinants…

Probability · Mathematics 2020-10-15 Alexei Borodin , Ivan Corwin , Daniel Remenik

This paper presents a two-phase method for learning interaction kernels of stochastic many-particle systems. After transforming stochastic trajectories of every particle into the particle density function by the kernel density estimation…

Computational Physics · Physics 2025-01-03 Yangxuan Shi , Wuyue Yang , Liu Hong

When the number of particles is finite, the noncolliding Brownian motion (the Dyson model) and the noncolliding squared Bessel process are determinantal diffusion processes for any deterministic initial configuration $\xi=\sum_{j \in…

Probability · Mathematics 2011-12-07 Makoto Katori , Hideki Tanemura

We show a diffusive upper bound on the transition probability of a tagged particle in the symmetric simple exclusion process. The proof relies on optimal spectral gap estimates for the dynamics in finite volume, which are of independent…

Probability · Mathematics 2018-04-27 Arianna Giunti , Yu Gu , Jean-Christophe Mourrat

In this paper we continue to investigate a certain class of Hankel-like positive definite kernels using their associated orthogonal polynomials. The main result of this paper is about the structure of this kind of kernels.

Functional Analysis · Mathematics 2007-05-23 T. Banks , T. Constantinescu

For a controllable linear time-varying (LTV) pair $(\boldsymbol{A}_t,\boldsymbol{B}_t)$ and $\boldsymbol{Q}_{t}$ positive semidefinite, we derive the Markov kernel for the It\^{o} diffusion…

Optimization and Control · Mathematics 2025-04-23 Alexis M. H. Teter , Wenqing Wang , Sachin Shivakumar , Abhishek Halder

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

As well as arising naturally in the study of non-intersecting random paths, random spanning trees, and eigenvalues of random matrices, determinantal point processes (sometimes also called fermionic point processes) are relatively easy to…

Probability · Mathematics 2008-04-04 Steven N. Evans , Alex Gottlieb

In this paper, we investigate a nonparametric approach to provide a recursive estimator of the transition density of a non-stationary piecewise-deterministic Markov process, from only one observation of the path within a long time. In this…

Statistics Theory · Mathematics 2013-05-07 Romain Azaïs

We introduce seven families of stochastic systems of interacting particles in one-dimension corresponding to the seven families of irreducible reduced affine root systems. We prove that they are determinantal in the sense that all…

Probability · Mathematics 2017-10-05 Makoto Katori

In this note we present new examples of determinantal point processes with infinitely many particles. The particles live on the half-lattice {1,2,...} or on the open half-line (0,+\infty). The main result is the computation of the…

Probability · Mathematics 2010-11-16 Leonid Petrov

We study the kernel estimator of the transition density of bifurcating Markov chains. Under some ergodic and regularity properties, we prove that this estimator is consistent and asymptotically normal. Next, in the numerical studies, we…

Statistics Theory · Mathematics 2023-03-28 S. Valère Bitseki Penda

In the paper the covariant kernel of the nucleon-nucleon interaction of particles with scalar propagators is analyzed. The Bethe-Salpeter equation for the T matrix is considered in the rank- one separable kernel. The parameters of the…

Nuclear Theory · Physics 2018-08-01 S. G. Bondarenko , V. V. Burov , S. A. Yurev

This note gives an explicit description of conditional measures for the determinantal point process with the Bergman kernel.

Probability · Mathematics 2022-01-03 Alexander I. Bufetov

We study iterations of integral kernels satisfying a transience-type condition and we prove exponential estimates analogous to Gronwall\rq{}s inequality. As a consequence we obtain estimates of Schr\"odinger perturbations of integral…

Functional Analysis · Mathematics 2012-08-17 Krzysztof Bogdan , Wolfhard Hansen , Tomasz Jakubowski
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