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Determinantal and permanental processes are point processes with a correlation function given by a determinant or a permanent. Their atoms exhibit mutual attraction of repulsion, thus these processes are very far from the uncorrelated…

Probability · Mathematics 2010-04-19 Isabelle Camilier , Laurent Decreusefond

The compound Poisson process and the Dirichlet process are the pillar structures of Renewal theory and Bayesian nonparametric theory, respectively. Both processes have many useful extensions to fulfill the practitioners needs to model the…

Applications · Statistics 2019-05-17 Arrigo Coen , Beatriz Godínez-Chaparro

Given a homogeneous Poisson process on ${\mathbb{R}}^d$ with intensity $\lambda$, we prove that it is possible to partition the points into two sets, as a deterministic function of the process, and in an isometry-equivariant way, so that…

Probability · Mathematics 2011-12-09 Alexander E. Holroyd , Russell Lyons , Terry Soo

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

In the focus of our attention is the asymptotic properties of the sequence of convex hulls which arise as a result of a peeling procedure applied to the convex hull generated by a Poisson point process. Processes of the considered type are…

Statistics Theory · Mathematics 2010-02-16 Youri Davydov , Alexender Nagaev , Anne Philippe

The random connection model is a random graph whose vertices are given by the points of a Poisson process and whose edges are obtained by randomly connecting pairs of Poisson points in a position dependent but independent way. We study…

Probability · Mathematics 2018-08-06 Günter Last , Franz Nestmann , Matthias Schulte

We show that any sequence $(x_n)_{n \in \mathbb{N}} \subseteq [0,1]$ that has Poissonian correlations of $k$-th order is uniformly distributed, also providing a quantitative description of this phenomenon. Additionally, we extend…

Number Theory · Mathematics 2022-09-26 Manuel Hauke , Agamemnon Zafeiropoulos

Kingman derived the Ewens sampling formula for random partitions describing the genetic variation in a neutral mutation model defined by a Poisson process of mutations along lines of descent governed by a simple coalescent process, and…

Probability · Mathematics 2007-05-23 Rui Dong , Alexander Gnedin , Jim Pitman

These lecture notes present some new concentration inequalities for Feynman-Kac particle processes. We analyze different types of stochastic particle models, including particle profile occupation measures, genealogical tree based evolution…

Numerical Analysis · Mathematics 2011-07-13 Pierre Del Moral , Peng Hu , Liming Wu

We construct a compound Poisson process conditioned on its random summation that represents the sizes of the connected components in the sparse Erd\H{o}s-R\'enyi random graph $G(n,c/n)$. This new representation depicts a connection between…

Probability · Mathematics 2023-10-23 Wen Sun

As part of a general theory for the isomorphism problem for actions of amenable groups, Ornstein and Weiss (J. Anal. Math. 48:1-141,1987) proved that any two Poisson point processes are isomorphic as measure-preserving actions. We give an…

Probability · Mathematics 2019-11-01 Terry Soo , Amanda Wilkens

In this paper we develop a representation formula of Clark-Ocone type for any integrable Poisson functionals, which extends the Poisson imbedding for point processes. This representation formula differs from the classical Clark-Ocone…

Probability · Mathematics 2024-04-12 Caroline Hillairet , Thomas Peyrat , Anthony Réveillac

We study a model of spatial random permutations over a discrete set of points. Formally, a permutation $\sigma$ is sampled proportionally to the weight $\exp\{-\alpha \sum_x V(\sigma(x)-x)\},$ where $\alpha>0$ is the temperature and $V$ is…

Probability · Mathematics 2019-04-09 Inés Armendáriz , Pablo A. Ferrari , Nicolás Frevenza

A Poisson system is a Poisson point process and a group action, together forming a measure-preserving dynamical system. Ornstein and Weiss proved Poisson systems over many amenable groups were isomorphic in their 1987 paper. We consider…

Dynamical Systems · Mathematics 2023-11-22 Amanda Wilkens

We are interested in phase transitions in certain percolation models on point processes and their dependence on clustering properties of the point processes. We show that point processes with smaller void probabilities and factorial moment…

Probability · Mathematics 2013-08-02 Bartlomiej Blaszczyszyn , D. Yogeshwaran

The theory of sparse stochastic processes offers a broad class of statistical models to study signals. In this framework, signals are represented as realizations of random processes that are solution of linear stochastic differential…

Probability · Mathematics 2017-02-17 Julien Fageot , Virginie Uhlmann , Michael Unser

Let X be a Poisson point process of intensity lambda on the real line. A thickening of it is a (deterministic) measurable function f such that the union of X and f(X) is a Poisson point process of intensity lambda' where lambda'>lambda. An…

Probability · Mathematics 2017-03-14 Ori Gurel-Gurevich , Ron Peled

Processes having the same bridges as a given reference Markov process constitute its {\it reciprocal class}. In this paper we study the reciprocal class of compound Poisson processes whose jumps belong to a finite set $\mathcal{A} \subset…

Probability · Mathematics 2014-07-01 Giovanni Conforti , Paolo Dai Pra , Sylvie Roelly

In [Schuhmacher, Electron. J. Probab. 10 (2005), 165--201] estimates of the Barbour-Brown distance d_2 between the distribution of a thinned point process and the distribution of a Poisson process were derived by combining discretization…

Probability · Mathematics 2007-05-23 Dominic Schuhmacher

A Neyman-Scott process is a special case of a Cox process. The latent and observable stochastic processes are both Poisson processes. We consider a deep Neyman-Scott process in this paper, for which the building components of a network are…

Machine Learning · Statistics 2022-05-10 Chengkuan Hong , Christian R. Shelton