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These notes rigorously construct the stochastic integral of a Hilbert Space valued process driven by a Cylindrical Brownian Motion. We expand upon this stochastic calculus to present an introduction to stochastic differential equations in…

Probability · Mathematics 2023-09-15 Daniel Goodair

Under mild conditions, it is possible to obtain, from almost purely measure-theoretic considerations and without any specific reference to stochastic processes, a change-of-measures result, resembling the usual Radon-Nikod\'ym change of…

Probability · Mathematics 2020-06-15 Yu-Lin Chou

This project investigates the approximate controllability of a class of stochastic integrodifferential equations in Hilbert space with non-local beginning conditions. In a departure from the conventional concerns expressed in the…

Optimization and Control · Mathematics 2026-02-10 Mamadou Pathe LY , Ravikumar Kasinathan , Ramkumar Kasinathan , Dimplekumar Chalishajar , Mamadou Abdoul Diop

This paper presents a brief survey of the theory of stochastic integration in Banach spaces. Expositions of the stochastic integrals in martingale type 2 spaces and UMD spaces are presented, as well as some applications of the latter to…

Probability · Mathematics 2014-05-28 Jan van Neerven , Mark Veraar , Lutz Weis

For a general free L\'evy process, we prove the existence of its higher variation processes as limits in distribution, and identify the limits in terms of the L\'evy-It\^o representation of the original process. For a general free compound…

Operator Algebras · Mathematics 2023-04-07 Michael Anshelevich , Zhichao Wang

Process convolutions yield random fields with flexible marginal distributions and dependence beyond Gaussianity, but statistical inference is often hampered by a lack of closed-form marginal distributions, and simulation-based inference may…

Methodology · Statistics 2017-10-19 Thomas Opitz

This paper contains a study of multivariate second order stochastic mappings indexed by an abstract set $\Lambda$ in close connection to their operator covariance functions. The characterizations of the normal Hilbert module or of Hilbert…

Functional Analysis · Mathematics 2015-01-27 Pastorel Gaspar , Lorena Popa

Within the framework of probability distributions on projective Hilbert space a scheme for the calculation of multitime correlation functions is developed. The starting point is the Markovian stochastic wave function description of an open…

Quantum Physics · Physics 2009-10-31 Heinz-Peter Breuer , Bernd Kappler , Francesco Petruccione

Here we present well-posedness results for first order stochastic differential inclusions, more precisely for sweeping process with a stochastic perturbation. These results are provided in combining both deterministic sweeping process…

Analysis of PDEs · Mathematics 2014-03-31 Frederic Bernicot , Juliette Venel

In this paper, we consider the composition of two independent processes : one process corresponds to position and the other one to time. Such processes will be called iterated processes. We first propose an algorithm based on the Euler…

Probability · Mathematics 2017-05-03 Michèle Thieullen , Alexis Vigot

Stochastic parabolic integro-differential problem is considered in the whole space. By verifying H\"ormander condition, the existence and uniqueness is proved in Lp-spaces of functions whose regularity is defined by a scalable Levy measure.…

Analysis of PDEs · Mathematics 2018-05-10 R. Mikulevicius , C. Phonsom

In this paper, we study numerical approximations for optimal control of a class of stochastic partial differential equations with partial observations. The system state evolves in a Hilbert space, whereas observations are given in…

Optimization and Control · Mathematics 2025-04-02 Feng Bao , Yanzhao Cao , Hongjiang Qian

In this paper, an integration by parts formula was derived for jump processes on Hilbert spaces. Using this formula, we investigated derivative formula and exponential ergodicity for nonlinear SPDEs driven by purely jump processes.

Probability · Mathematics 2013-08-26 Yulin Song , Tiange Xu

We study numerical integration of functions depending on an infinite number of variables. We provide lower error bounds for general deterministic linear algorithms and provide matching upper error bounds with the help of suitable multilevel…

Numerical Analysis · Mathematics 2021-02-09 Josef Dick , Michael Gnewuch

We study the approximation of a square-integrable function from a finite number of evaluations on a random set of nodes according to a well-chosen distribution. This is particularly relevant when the function is assumed to belong to a…

Machine Learning · Statistics 2024-11-13 Ayoub Belhadji , Rémi Bardenet , Pierre Chainais

Probabilistic programming is related to a compositional approach to stochastic modeling by switching from discrete to continuous time dynamics. In continuous time, an operator-algebra semantics is available in which processes proceeding in…

Artificial Intelligence · Computer Science 2012-12-05 Eric Mjolsness

This work focuses on topics related to Hamiltonian stochastic differential equations with L\'{e}vy noise. We first show that the phase flow of the stochastic system preserves symplectic structure, and propose a stochastic version of…

Dynamical Systems · Mathematics 2019-07-24 Pingyuan Wei , Ying Chao , Jinqiao Duan

Let the process Y(t) be a Skorohod integral process with respect to Brownian motion. We use a recent result by Tudor (2004), to prove that Y(t) can be represented as the limit of linear combinations of processes that are products of forward…

Probability · Mathematics 2016-08-16 Giovanni Peccati , Michèle Thieullen , Ciprian A. Tudor

Two aspects of noncolliding diffusion processes have been extensively studied. One of them is the fact that they are realized as harmonic Doob transforms of absorbing particle systems in the Weyl chambers. Another aspect is integrability in…

Probability · Mathematics 2014-07-18 Makoto Katori

Motivated by the construction of the It\^o stochastic integral, we consider a step function method to discretize and simulate volatility modulated L\'evy semistationary processes. Moreover, we assess the accuracy of the method with a…

Applications · Statistics 2014-07-11 Mikkel Bennedsen , Asger Lunde , Mikko S. Pakkanen