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In the article, integration of temporal functions in (possibly non-UMD) Banach spaces with respect to (possibly non-Gaussian) fractional processes from a finite sum of Wiener chaoses is treated. The family of fractional processes that is…

Probability · Mathematics 2020-12-18 Petr Čoupek , Bohdan Maslowski , Martin Ondreját

This paper provides convergence analysis for the approximation of a class of path-dependent functionals underlying a continuous stochastic process. In the first part, given a sequence of weak convergent processes, we provide a sufficient…

Probability · Mathematics 2013-07-22 Qingshuo Song , George Yin , Qing Zhang

The Bou\'e-Dupuis variational formula gives a representation for log Laplace transforms of bounded measurable functions of a finite dimensional Brownian motion on a compact time interval as an infimum of a suitable cost over a collection of…

Probability · Mathematics 2024-03-05 A. Budhiraja

We define various higher-order Markov properties for stochastic processes $(X(t))_{t\in \mathbb{T}}$, indexed by an interval $\mathbb{T} \subseteq \mathbb{R}$ and taking values in a real and separable Hilbert space $U$. We furthermore…

Probability · Mathematics 2026-01-06 Kristin Kirchner , Joshua Willems

It is well known that Brownian motion enjoys several distributional invariances such as the scaling property and the time reversal. In this paper, we prove another invariance of Brownian motion that is compatible with the time reversal. The…

Probability · Mathematics 2023-10-20 Yuu Hariya

Functionals that strive to correct for such self-interaction errors, such as those obtained by imposing the Perdew-Zunger self-interaction correction or the generalized Koopmans' condition, become orbital dependent or orbital-density…

Materials Science · Physics 2011-08-30 Cheol-Hwan Park , Andrea Ferretti , Ismaila Dabo , Nicolas Poilvert , Nicola Marzari

A new non-perturbative approach to quantum theory in curved spacetime and to quantum gravity, based on a generalisation of the Wigner equation, is proposed. Our definition for a Wigner equation differs from what have otherwise been…

High Energy Physics - Theory · Physics 2009-10-30 Frank Antonsen

We have recently presented an extension of the standard variational calculus to include the presence of deformed derivatives in the Lagrangian of a system of particles and in the Lagrangian density of field-theoretic models. Classical…

Mathematical Physics · Physics 2017-06-30 J. Weberszpil , J. A. Helayël-Neto

We study the asymptotic behaviour of a properly normalized time changed Wiener processes. The time change reflects the fact that we consider the Laplace operator (which generates a Wiener process) multiplied by a possibly degenerate…

Probability · Mathematics 2020-05-11 Yuri Kondratiev , Yuliya Mishura , René L. Schilling

This paper presents a self-contained new theory of weak fractional differential calculus in one-dimension. The crux of this new theory is the introduction of a weak fractional derivative notion which is a natural generalization of integer…

Functional Analysis · Mathematics 2020-07-21 Xiaobing Feng , Mitchell Sutton

We have shown recently that a Markov process conditioned on rare events involving time-integrated random variables can be described in the long-time limit by an effective Markov process, called the driven process, which is given…

Statistical Mechanics · Physics 2015-12-17 Raphael Chetrite , Hugo Touchette

We investigate piecewise-linear stochastic models as with regards to the probability distribution of functionals of the stochastic processes, a question which occurs frequently in large deviation theory. The functionals that we are looking…

Statistical Mechanics · Physics 2015-06-22 Yaming Chen , Wolfram Just

We prove an invariance principle for non-stationary random processes and establish a rate of convergence under a new type of mixing condition. The dependence is exponentially decaying in the gap between the past and the future and is…

Probability · Mathematics 2024-12-23 Ion Grama , Émile Le Page , Marc Peigné

Donsker Theorem is perhaps the most famous invariance principle result for Markov processes. It states that when properly normalized, a random walk behaves asymptotically like a Brownian motion. This approach can be extended to general…

Probability · Mathematics 2020-05-29 Eustache Besançon , E Besanç On , Laurent Decreusefond , Pascal Moyal

We study the potential theory of a large class of infinite dimensional L\'evy processes, including Brownian motion on abstract Wiener spaces. The key result is the construction of compact Lyapunov functions, i.e. excessive functions with…

Probability · Mathematics 2010-07-27 Lucian Beznea , Aurel Cornea , Michael Röckner

We analyze the statistical properties of a temporal point process driven by a confined fractional Brownian motion. The event count distribution and power spectral density of this non--Markovian point process exhibit power--law scaling. We…

Statistical Mechanics · Physics 2022-08-31 Aleksejus Kononovicius , Rytis Kazakevičius , Bronislovas Kaulakys

Using the Euler--Maruyama technique, we show that a class of Wiener processes exist that are obtained by computing an arbitrary positive power of them. This can be accomplished with a proper set of definitions that makes meaningful the…

Mathematical Physics · Physics 2017-08-28 Marco Frasca , Alfonso Farina

The optimal protocols for the irreversible work achieve their maximum usefulness if their work fluctuations are the smallest ones. In this work, for classical and isothermal processes subjected to finite-time and weak drivings, I show that…

Statistical Mechanics · Physics 2023-11-14 Pierre Nazé

In this paper, we introduce two new matrix stochastic processes: fractional Wishart processes and $\varepsilon$-fractional Wishart processes with integer indices which are based on the fractional Brownian motions and then extend…

Optimization and Control · Mathematics 2017-05-16 Jia Yue , Nan-jing Huang

Modeling the temporal behavior of data is of primordial importance in many scientific and engineering fields. Baseline methods assume that both the dynamic and observation equations follow linear-Gaussian models. However, there are many…

Machine Learning · Computer Science 2020-11-03 Xavier Alameda-Pineda , Vincent Drouard , Radu Horaud