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While distributed systems with transfer of processes have become pervasive, methods for reasoning about their behaviour are underdeveloped. In this paper we propose a bisimulation technique for proving behavioural equivalence of such…

Logic in Computer Science · Computer Science 2011-05-09 Adrien Piérard , Eijiro Sumii

We develop a framework for Gaussian processes regression constrained by boundary value problems. The framework may be applied to infer the solution of a well-posed boundary value problem with a known second-order differential operator and…

Machine Learning · Computer Science 2020-12-23 Mamikon Gulian , Ari Frankel , Laura Swiler

We describe a general operational method that can be used in the analysis of fractional initial and boundary value problems with additional analytic conditions. As an example, we derive analytic solutions of some fractional generalisation…

Analysis of PDEs · Mathematics 2013-04-04 Roberto Garra , Federico Polito

We study well-posedness of sweeping processes with stochastic perturbations generated by a fractional Brownian motion and convergence of associated numerical schemes. To this end, we first prove new existence, uniqueness and approximation…

Classical Analysis and ODEs · Mathematics 2015-05-07 Adrian Falkowski , Leszek Slominski

In this paper we give some coupled fixed point results for mappings satisfying different contractive conditions on complete partial metric spaces.

General Topology · Mathematics 2016-10-05 Hassen Aydi

This paper is concerned with combined inference for point processes on the real line observed in a broken interval. For such processes, the classic history-based approach cannot be used. Instead, we adapt tools from sequential spatial point…

Methodology · Statistics 2015-06-04 M. N. M. van Lieshout

Shapley values have become a cornerstone of explainable AI, but they are computationally expensive to use, especially when features are dependent. Evaluating them requires approximating a large number of conditional expectations, either via…

Artificial Intelligence · Computer Science 2026-02-11 Lars Henry Berge Olsen , Dennis Christensen

We discuss various infinite-dimensional configuration spaces that carry measures quasiinvariant under compactly-supported diffeomorphisms of a manifold M corresponding to a physical space. Such measures allow the construction of unitary…

Mathematical Physics · Physics 2009-11-10 Gerald A. Goldin , Ugo Moschella , Takao Sakuraba

The goal of this paper is to clarify when a stochastic partial differential equation with an affine realization admits affine state processes. This includes a characterization of the set of initial points of the realization. Several…

Probability · Mathematics 2025-11-21 Stefan Tappe

This paper describes three methods for carrying out non-asymptotic inference on partially identified parameters that are solutions to a class of optimization problems. Applications in which the optimization problems arise include estimation…

Methodology · Statistics 2022-12-02 Joel L. Horowitz , Sokbae Lee

We define and study fractional versions of the well-known Gamma subordinator $\Gamma :=\{\Gamma (t),$ $t\geq 0\},$ which are obtained by time-changing $% \Gamma $ by means of an independent stable subordinator or its inverse. Their…

Probability · Mathematics 2013-05-09 Luisa Beghin

The separate tasks of denoising, least squares expectation, and manifold learning can often be posed in a common setting of finding the conditional expectations arising from a product of two random variables. This paper focuses on this more…

Machine Learning · Statistics 2024-02-15 Suddhasattwa Das

Many random combinatorial objects have a component structure whose joint distribution is equal to that of a process of mutually independent random variables, conditioned on the value of a weighted sum of the variables. It is interesting to…

Probability · Mathematics 2013-08-16 Richard Arratia , Simon Tavare

This tutorial provides an introduction to Palm distributions for spatial point processes. Initially, in the context of finite point processes , we give an explicit definition of Palm distributions in terms of their density functions. Then…

Statistics Theory · Mathematics 2016-06-20 Jean-François Coeurjolly , Jesper Møller , Rasmus Waagepetersen

We consider the symplectic induced Ginibre process, which is a Pfaffian point process on the plane. Let $N$ be the number of points. We focus on the almost-circular regime where most of the points lie in a thin annulus $\mathcal{S}_{N}$ of…

Mathematical Physics · Physics 2022-06-14 Sung-Soo Byun , Christophe Charlier

The article presents a systematic study of the problem of conditioning a Gaussian random variable $\xi$ on nonlinear observations of the form $F \circ \phi(\xi)$ where $\phi: \mathcal{X} \to \mathbb{R}^N$ is a bounded linear operator and…

Machine Learning · Statistics 2024-05-24 Yifan Chen , Bamdad Hosseini , Houman Owhadi , Andrew M Stuart

Under the assumption that data lie on a compact (unknown) manifold without boundary, we derive finite sample bounds for kernel smoothing and its (first and second) derivatives, and we establish asymptotic normality through Berry-Esseen type…

Statistics Theory · Mathematics 2026-01-26 Eunseong Bae , Wolfgang Polonik

Kernels of $\alpha$-permanental processes of the form \[ v(x,y)=u(x,y)+f(y),\qquad x,y\in S, \] in which $u(x,y)$ is symmetric, and $f$ is an excessive function for the Borel right process with potential densities $u(x,y)$, are considered.…

Probability · Mathematics 2018-02-23 Michael B. Marcus , Jay Rosen

In this article, we study the extremal processes of branching Brownian motions conditioned on having an unusually large maximum. The limiting point measures form a one-parameter family and are the decoration point measures in the extremal…

Probability · Mathematics 2020-09-01 Julien Berestycki , Éric Brunet , Aser Cortines , Bastien Mallein

A new type of dependent thinning for point processes in continuous space is proposed, which leverages the advantages of determinantal point processes defined on finite spaces and, as such, is particularly amenable to statistical, numerical,…

Machine Learning · Computer Science 2019-06-19 Bartłomiej Błaszczyszyn , Paul Keeler