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Fractional Brownian motion is a non-Markovian Gaussian process indexed by the Hurst exponent $H\in [0,1]$, generalising standard Brownian motion to account for anomalous diffusion. Functionals of this process are important for practical…

Statistical Mechanics · Physics 2021-11-24 Tridib Sadhu , Kay Jörg Wiese

This paper considers the orthogonal expansion of the fractional Brownian motion relative to the Legendre polynomials. Such an expansion has not only theoretical but also practical interest, since it can be applied to approximate and…

Probability · Mathematics 2026-01-13 Konstantin A. Rybakov

Brownian motion in one or more dimensions is extensively used as a stochastic process to model natural and engineering signals, as well as financial data. Most works dealing with multidimensional Brownian motion consider the different…

Statistical Mechanics · Physics 2025-03-10 Michał Balcerek , Adrian Pacheco-Pozo , Agnieszka Wyłomanska , Krzysztof Burnecki , Diego Krapf

Fractional Brownian motion belongs to a class of long memory Gaussian processes that can be represented as linear functionals of an infinite dimensional Markov process. This representation leads naturally to: - An efficient algorithm to…

Probability · Mathematics 2007-05-23 Philippe Carmona , Laure Coutin

Fractional Brownian motion (FBM), a non-Markovian self-similar Gaussian stochastic process with long-ranged correlations, represents a widely applied, paradigmatic mathematical model of anomalous diffusion. We report the results of…

This paper deals with the identification of the multivariate fractional Brownian motion, a recently developed extension of the fractional Brownian motion to the multivariate case. This process is a $p$-multivariate self-similar Gaussian…

Statistics Theory · Mathematics 2011-11-16 Pierre-Olivier Amblard , Jean-François Coeurjolly

Fractional Brownian motion (fBm) is a canonical model for long-memory phenomena. In the presence of large amounts of potentially memory-bearing data, the data are often averaged, which can change the structure of the underlying…

We introduce the idea of {\it collisional models} for Brownian particles, in which a particle is sequentially placed in contact with distinct thermal environments and external forces. Thermodynamic properties are exactly obtained,…

Statistical Mechanics · Physics 2020-10-07 Angel L. L. Stable , Carlos E. F. Noa , William G. C. Oropesa , C. E. Fiore

Herein we develop a dynamical foundation for fractional Brownian Motion. A clear relation is established between the asymptotic behaviour of the correlation function and diffusion in a dynamical system. Then, assuming that scaling is…

chao-dyn · Physics 2008-02-03 R Mannella , P Grigolini , BJ West

Many real time-series exhibit behavior adequate to long range dependent data. Additionally very often these time-series have constant time periods and also have characteristics similar to Gaussian processes although they are not Gaussian.…

Data Analysis, Statistics and Probability · Physics 2017-01-04 A. Kumar , A. Wyłomańska , R. Połoczański , S. Sundar

The dynamical behavior for a quantum Brownian particle is investigated under a random potential of the fractional iterative map on a one-dimensional lattice. For our case, the quantum expectation values can be obtained numerically from the…

Statistical Mechanics · Physics 2007-05-23 Kyungsik Kim , Y. S. Kong , M. K. Yum , J. T. Kim

We investigate fractional Brownian motion with a microscopic random-matrix model and introduce a fractional Langevin equation. We use the latter to study both sub- and superdiffusion of a free particle coupled to a fractal heat bath. We…

Statistical Mechanics · Physics 2009-11-07 E. Lutz

We consider fractional Brownian motion with the Hurst parameters from (1/2,1). We found that the increment of a fractional Brownian motion can be represented as the sum of a two independent Gaussian processes one of which is smooth in the…

Probability · Mathematics 2015-10-14 Nikolai Dokuchaev

We define a time dependent empirical process based on $n$ i.i.d.~fractional Brownian motions and establish Gaussian couplings and strong approximations to it by Gaussian processes. They lead to functional laws of the iterated logarithm for…

Probability · Mathematics 2016-06-21 Péter Kevei , David M. Mason

We study fractional Brownian motion (fBm) characterized by the Hurst exponent H. Using a Monte Carlo sampling technique, we are able to numerically generate fBm processes with an absorbing boundary at the origin at discrete times for a…

Statistical Mechanics · Physics 2015-06-15 Alexander K. Hartmann , Satya N. Majumdar , Alberto Rosso

In certain applications, for instance biomechanics, turbulence, finance, or Internet traffic, it seems suitable to model the data by a generalization of a fractional Brownian motion for which the Hurst parameter $H$ is depending on the…

Statistics Theory · Mathematics 2007-06-13 Jean-Marc Bardet , Pierre Bertrand

The conformational properties of flexible and semiflexible polymers exposed to active noise are studied theoretically. The noise may originate from the interaction of the polymer with surround- ing active (Brownian) particles or from the…

Soft Condensed Matter · Physics 2024-06-03 Thomas Eisenstecken , Gerhard Gompper , Roland G. Winkler

Under certain mild conditions, some limit theorems for functionals of two independent Gaussian processes are obtained. The results apply to general Gaussian processes including fractional Brownian motion, sub-fractional Brownian motion and…

Probability · Mathematics 2018-01-30 Jian Song , Fangjun Xu , Qian Yu

Fractional Brownian motion (fBm) is a centered self-similar Gaussian process with stationary increments, which depends on a parameter $H \in (0, 1)$ called the Hurst index. The use of time-changed processes in modeling often requires the…

Probability · Mathematics 2014-08-21 Jebessa B. Mijena

We introduce fractional Brownian motion processes (fBm) as an alternative model for the turbulent index of refraction. These processes allow to reconstruct most of the index properties, but they are not differentiable. We overcome the…

Optics · Physics 2007-05-23 Dario G Perez