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In this paper, a class of piecewise deterministic Markov processes with underlying fast dynamic is studied. Using a "penalty method" , an averaging result is obtained when the underlying dynamic is infinitely accelerated. The features of…

Probability · Mathematics 2016-08-31 Alexandre Genadot

In this short note, we extend to the continuous case a mean projection theorem for discrete determinantal point processes associated with a finite range projection, thus strengthening a known result in random linear algebra due to Ermakov…

Probability · Mathematics 2023-08-22 Adrien Kassel , Thierry Lévy

We develop a domain-theoretic framework for imprecise probability reasoning and inference on general topological spaces with a countably based continuous lattice of open sets. We address two distinct forms of uncertainty: partial or…

Logic in Computer Science · Computer Science 2026-04-13 Abbas Edalat , Pietro Di Gianantonio , Amin Farjudian

Construct a random set by independently selecting each finite subset of the integers with some probability depending on the set up to translations and taking the union of the selected sets. We show that when the only sets selected with…

Probability · Mathematics 2025-06-06 Yinon Spinka

Determinantal point processes (DPPs) are an important concept in random matrix theory and combinatorics. They have also recently attracted interest in the study of numerical methods for machine learning, as they offer an elegant "missing…

Machine Learning · Computer Science 2018-04-18 Philipp Hennig , Roman Garnett

This paper establishes the theoretical foundation for statistical applications of an intriguing new type of spatial point processes called critical point processes. These point processes, residing in Euclidean space, consist of the critical…

Probability · Mathematics 2025-07-08 Julien Chevallier , Jean-François Coeurjolly , Rasmus Waagepetersen

We consider a new class of determinantal point processes in the complex plane coming from the ground state of free fermions associated with Berezin--Toeplitz operators. These processes generalize the Ginibre ensemble from random matrix…

Probability · Mathematics 2025-08-15 Alix Deleporte , Gaultier Lambert

In this paper, we establish a general setup for studying incidence-preserving motions of projective geometric configurations of points and lines via a "projective rigidity matrix". The spaces of infinitesimal motions of a point-line…

We consider a class of stochastic dynamical systems, called piecewise deterministic Markov processes, with states $(x, \s)\in \O\times \G$, $\O$ being a region in $\bbR^d$ or the $d$--dimensional torus, $\G$ being a finite set. The…

Statistical Mechanics · Physics 2009-02-25 Alessandra Faggionato , Davide Gabrielli , Marco Ribezzi Crivellari

We consider a class of piecewise-deterministic Markov processes where the state evolves according to a linear dynamical system. This continuous time evolution is interspersed by discrete events that occur at random times and change (reset)…

Systems and Control · Computer Science 2017-11-15 Mohammad Soltani , Abhyudai Singh

Determinantal point processes (DPPs) are a class of repulsive point processes, popular for their relative simplicity. They are traditionally defined via their marginal distributions, but a subset of DPPs called "L-ensembles" have tractable…

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

We study a 2-parametric family of probability measures on the space of countable point configurations on the punctured real line (the points of the random configuration are concentrated near zero). These measures (or, equivalently, point…

Representation Theory · Mathematics 2007-05-23 Alexei Borodin

Piecewise Deterministic Markov Processes (PDMPs) are studied in a general framework. First, different constructions are proven to be equivalent. Second, we introduce a coupling between two PDMPs following the same differential flow which…

Probability · Mathematics 2021-08-03 Alain Durmus , Arnaud Guillin , Pierre Monmarché

We consider Markov decision processes (MDPs) which are a standard model for probabilistic systems. We focus on qualitative properties for MDPs that can express that desired behaviors of the system arise almost-surely (with probability 1) or…

Logic in Computer Science · Computer Science 2014-05-06 Krishnendu Chatterjee , Martin Chmelik , Przemyslaw Daca

We introduce the concept of structured synthesis for Markov decision processes where the structure is induced from finitely many pre-specified options for a system configuration. The resulting synthesis problem is in general a nonlinear…

Software Engineering · Computer Science 2018-07-18 Nils Jansen , Laura Humphrey , Jana Tumova , Ufuk Topcu

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

A new sufficient condition is proved for the existence of stochastic semigroups generated by the sum of two unbounded operators. It is applied to one-dimensional piecewise deterministic Markov processes, where we also discuss the existence…

Analysis of PDEs · Mathematics 2009-07-07 Michael C. Mackey , Marta Tyran-Kaminska

We prove the Bernoulli property for determinantal point processes on $ \mathbb{R}^d $ with translation-invariant kernels. For the determinantal point processes on $ \mathbb{Z}^d $ with translation-invariant kernels, the Bernoulli property…

Probability · Mathematics 2019-09-17 Shota Osada

In this paper, we propose a new comparison tool for spatial homogeneity of point processes, based on the joint examination of void probabilities and factorial moment measures. We prove that determinantal and permanental processes, as well…

Probability · Mathematics 2014-04-23 Bartlomiej Blaszczyszyn , D. Yogeshwaran

We study the problem of solving fixed-point equations for seminorm-contractive operators and establish foundational results on the non-asymptotic behavior of iterative algorithms in both deterministic and stochastic settings. Specifically,…

Machine Learning · Computer Science 2025-02-21 Zaiwei Chen , Sheng Zhang , Zhe Zhang , Shaan Ul Haque , Siva Theja Maguluri
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