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This article introduces a novel construction of the two-dimensional fractional Brownian motion (2D fBm) with dependent components. Unlike similar models discussed in the literature, our approach uniquely accommodates the full range of model…

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 refractive index properties, but they are not differentiable. We…

Optics · Physics 2007-05-23 Dario G. Perez

Stochastic process exhibiting power-law slopes in the frequency domain are frequently well modeled by fractional Brownian motion (fBm). In particular, the spectral slope at high frequencies is associated with the degree of small-scale…

Methodology · Statistics 2017-09-13 J. M. Lilly , A. M. Sykulski , J. J Early , S. C. Olhede

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

Fractional Brownian motion (FBM) is the only Gaussian self-similar process with stationary increments. Its increment process, called fractional Gaussian noise, is ergodic and exhibits a property of power-like decaying autocorrelation…

Statistics Theory · Mathematics 2024-07-10 Michal Balcerek , Krzysztof Burnecki

Fractional Brownian motion (fBm) features both randomness and strong scale-free correlations, challenging generative models to reproduce the intrinsic memory characterizing the underlying stochastic process. Here we examine a zoo of…

Computer Vision and Pattern Recognition · Computer Science 2025-06-03 Alexander Lobashev , Dmitry Guskov , Kirill Polovnikov

We examine two stochastic processes with random parameters, which in their basic versions (i.e., when the parameters are fixed) are Gaussian and display long range dependence and anomalous diffusion behavior, characterized by the Hurst…

Probability · Mathematics 2024-10-16 Hubert Woszczek , Agnieszka Wylomanska , Aleksei Chechkin

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…

A major open problem in biophysics is to understand the highly heterogeneous transport of many structures inside living cells, such as endosomes. We find that mathematically it is described by spatio-temporal heterogeneous fractional…

Subcellular Processes · Quantitative Biology 2021-07-20 Nickolay Korabel , Daniel Han , Alessandro Taloni , Gianni Pagnini , Sergei Fedotov , Viki Allan , Thomas Andrew Waigh

The stochastic trajectories of molecules in living cells, as well as the dynamics in many other complex systems, often exhibit memory in their path over long periods of time. In addition, these systems can show dynamic heterogeneities due…

Statistical Mechanics · Physics 2024-07-10 Michał Balcerek , Agnieszka Wyłomańska , Krzysztof Burnecki , Ralf Metzler , Diego Krapf

The effects of a "diffusing diffusivity" (DD), a stochastically time-varying diffusion coefficient, are explored within the frameworks of three different forms of fractional Brownian motion (FBM): (i) the Langevin equation driven by…

Statistical Mechanics · Physics 2025-04-29 Wei Wang , Aleksei V. Chechkin , Ralf Metzler

The $d$-dimensional fractional Brownian motion (FBM for short) $B_t=((B_t^{(1)},...,B_t^{(d)}),t\in\mathbb{R})$ with Hurst exponent $\alpha$, $\alpha\in(0,1)$, is a $d$-dimensional centered, self-similar Gaussian process with covariance…

Probability · Mathematics 2009-06-23 Jérémie Unterberger

We introduce the first continuous-time score-based generative model that leverages fractional diffusion processes for its underlying dynamics. Although diffusion models have excelled at capturing data distributions, they still suffer from…

The analysis of local minima in time series data and random landscapes is essential across numerous scientific disciplines, offering critical insights into system dynamics. Recently, Kundu, Majumdar, and Schehr derived the exact…

Statistical Mechanics · Physics 2026-03-19 Maxim Dolgushev , Olivier Bénichou

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

This paper reviews and extends some recent results on the multivariate fractional Brownian motion (mfBm) and its increment process. A characterization of the mfBm through its covariance function is obtained. Similarly, the correlation and…

In this paper we study a parametric class of stochastic processes to model both fast and slow anomalous diffusion. This class, called generalized grey Brownian motion (ggBm), is made up off self-similar with stationary increments processes…

Mathematical Physics · Physics 2009-11-13 Antonio Mura , Gianni Pagnini

Fractional Brownian motion (fBm) is an experimentally-relevant, non-Markovian Gaussian stochastic process with long-ranged correlations between the increments, parametrised by the so-called Hurst exponent $H$; depending on its value the…

Statistical Mechanics · Physics 2023-10-04 O. Benichou , G. Oshanin

Diffusion through semipermeable structures arises in a wide range of processes in the physical and life sciences. Examples at the microscopic level range from artificial membranes for reverse osmosis to lipid bilayers regulating molecular…

Statistical Mechanics · Physics 2023-01-11 Paul C Bressloff

We study fractional Laplace motion (FLM) obtained from subordination of fractional Brownian motion to a gamma process, in the presence of an external drift that acts on the composite process or of an internal drift acting solely on the…

Statistical Mechanics · Physics 2024-12-20 Wei Wang , Yingjie Liang , Aleksei V. Chechkin , Ralf Metzler