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We present a new class of cluster point process models, which we call determinantal shot noise Cox processes (DSNCP), with repulsion between cluster centres. They are the special case of generalized shot noise Cox processes where the…

Methodology · Statistics 2022-05-31 Jesper Møller , Ninna Vihrs

We consider a collection of weighted Euclidian random balls in R^d distributed according a determinantal point process. We perform a zoom-out procedure by shrinking the radii while increasing the number of balls. We observe that the…

Probability · Mathematics 2019-07-24 Adrien Clarenne

Clustering observations across partially exchangeable groups of data is a routine task in Bayesian nonparametrics. Previously proposed models allow for clustering across groups by sharing atoms in the group-specific mixing measures.…

Methodology · Statistics 2025-10-17 Alessandro Carminati , Mario Beraha , Federico Camerlenghi , Alessandra Guglielmi

We consider the Boolean model with random radii based on Cox point processes. Under a condition of stabilization for the random environment, we establish existence and non-existence of subcritical regimes for the size of the cluster at the…

Probability · Mathematics 2020-05-26 Benedikt Jahnel , András Tóbiás , Elie Cali

We consider a collection of Euclidean random balls in ${\Bbb R}^d$ generated by a determinantal point process inducing interaction into the balls. We study this model at a macros\-copic level obtained by a zooming-out and three different…

Probability · Mathematics 2017-06-02 Jean-Christophe Breton , Adrien Clarenne , Renan Gobard

A new discrete-time shot noise Cox process for spatiotemporal data is proposed. The random intensity is driven by a dependent sequence of latent gamma random measures. Some properties of the latent process are derived, such as an…

Methodology · Statistics 2023-08-17 Federico Bassetti , Roberto Casarin , Matteo Iacopini

We consider a stationary random field indexed by an increasing sequence of subsets of $\mathbb{Z}^d$ obeying a very broad geometrical assumption on how the sequence expands. Under certain mixing and local conditions, we show how the tail…

Probability · Mathematics 2022-01-19 Anders Rønn-Nielsen , Mads Stehr

We study the structure of extreme level sets of a standard one dimensional branching Brownian motion, namely the sets of particles whose height is within a fixed distance from the order of the global maximum. It is well known that such…

Probability · Mathematics 2019-02-22 Aser Cortines , Lisa Hartung , Oren Louidor

We show that the `shot noise' bias in angular clustering power spectra observed from discrete samples of points is not noise, but rather a known additive contribution that naturally arises due to degenerate pairs of points. In particular,…

Cosmology and Nongalactic Astrophysics · Physics 2025-10-15 Nicolas Tessore , Alex Hall

This work is a continuation of the manuscript "the structure of extreme level sets in branching Brownian motion", in which the same authors studied the fine structure of the extreme level sets of branching Brownian motion, namely the sets…

Probability · Mathematics 2019-02-25 Aser Cortines , Lisa Hartung , Oren Louidor

Poisson shot noise processes are natural generalizations of compound Poisson processes that have been widely applied in insurance, neuroscience, seismology, computer science and epidemiology. In this paper we study sharp deviations,…

Probability · Mathematics 2021-08-12 Giovanni Luca Torrisi , Emilio Leonardi

The classic Vicsek model [Phys.Rev.Lett. {\bf75},1226(1995)] is studied in the regime of very low noise intensities, which is shown to be characterized by a cluster (MC) that contains a macroscopic fraction of the system particles. It is…

Computational Physics · Physics 2018-09-26 Lucas Barberis

This paper presents a general approach to linear stochastic processes driven by various random noises. Mathematically, such processes are described by linear stochastic differential equations of arbitrary order (the simplest non-trivial…

Condensed Matter · Physics 2009-10-28 Alon Drory

Heuristics indicate that point processes exhibiting clustering of points have larger critical radius $r_c$ for the percolation of their continuum percolation models than spatially homogeneous point processes. It has already been shown, and…

Probability · Mathematics 2015-03-19 B. Blaszczyszyn , D. Yogeshwaran

The problem of change-point estimation is considered under a general framework where the data are generated by unknown stationary ergodic process distributions. In this context, the consistent estimation of the number of change-points is…

Machine Learning · Statistics 2013-02-15 Azaden Khaleghi , Daniil Ryabko

We investigate continuum percolation for Cox point processes, that is, Poisson point processes driven by random intensity measures. First, we derive sufficient conditions for the existence of non-trivial sub- and super-critical percolation…

Probability · Mathematics 2017-11-01 Christian Hirsch , Benedikt Jahnel , Elie Cali

We propose a new model of cluster growth according to which the probability that a new unit is placed in a point at a distance $r$ from the city center is a Gaussian with mean equal to the cluster radius and variance proportional to the…

Physics and Society · Physics 2007-05-23 M. Pica Ciamarra , A. Coniglio

We study a two-species bidirectional exclusion process, and a single species variant, which is motivated by the motion of organelles and vesicles along microtubules. Specifically, we are interested in the clustering of the particles and…

Statistical Mechanics · Physics 2020-03-17 Jim Chacko , Sudipto Muhuri , Goutam Tripathy

In this contribution, we investigate the scaling of the distribution of the shot noise process, its power spectral density and its time above threshold.

Statistical Mechanics · Physics 2019-06-25 Audun Theodorsen

In many applications of X-ray computed tomography, an unsupervised segmentation of the reconstructed 3D volumes forms an important step in the image processing chain for further investigation of the digitized object. Therefore, the goal is…

Computer Vision and Pattern Recognition · Computer Science 2023-03-09 Thomas Lang
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