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It has been established under very general conditions that the ergodic properties of Markov processes are inherited by their conditional distributions given partial information. While the existing theory provides a rather complete picture…

Probability · Mathematics 2015-02-04 Patrick Rebeschini , Ramon van Handel

In a recent paper, Michael J. Saxton proposes to interpret as anomalous diffusion the occurrence of apparent transient sub-diffusive regimes in mean-squared displacements (MSD) plots, calculated from experimental trajectories of molecules…

Subcellular Processes · Quantitative Biology 2009-11-13 Nicolas Destainville , Aude Sauliere , Laurence Salome

Combining extensive single particle tracking microscopy data of endogenous lipid granules in living fission yeast cells with analytical results we show evidence for anomalous diffusion and weak ergodicity breaking. Namely we demonstrate…

A general theory of efficient estimation for ergodic diffusion processes sampled at high frequency with an infinite time horizon is presented. High frequency sampling is common in many applications, with finance as a prominent example. The…

Statistics Theory · Mathematics 2024-01-10 Michael Sørensen

Recent advances in light microscopy have spawned new research frontiers in microbiology by working around the diffraction barrier and allowing for the observation of nanometric biological structures. Microrheology is the study of the…

Probability · Mathematics 2016-07-27 Gustavo Didier , Kui Zhang

Heterogeneous diffusion processes are prevalent in various fields, including the motion of proteins in living cells, the migratory movement of birds and mammals, and finance. These processes are often characterized by time-varying dynamics,…

Statistical Mechanics · Physics 2025-03-11 Michał Balcerek , Adrian Pacheco-Pozo , Agnieszka Wyłomańska , Diego Krapf

Given a heterogeneous time-series sample, the objective is to find points in time (called change points) where the probability distribution generating the data has changed. The data are assumed to have been generated by arbitrary unknown…

Machine Learning · Statistics 2015-05-13 Azadeh Khaleghi , Daniil Ryabko

Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a…

Nonstandard ergodic averages can be defined for a measure-preserving action of a group on a probability space, as a natural extension of classical (nonstandard) ergodic averages. We extend the one-dimensional theory, obtaining L^1 pointwise…

Dynamical Systems · Mathematics 2012-06-21 Patrick LaVictoire , Andrew Parrish , Joseph Rosenblatt

We study the ergodic and non-ergodic behaviors of a clean Jaynes-Cummings-Hubbard chain for different parameters based on the average level spacings and the generalized fractal dimensions of eigenstates by using exact diagonalization. It…

Quantum Physics · Physics 2022-05-18 Jin-Lou Ma , Qing Li , Lei Tan

The characterization of diffusion processes is a keystone in our understanding of a variety of physical phenomena. Many of these deviate from Brownian motion, giving rise to anomalous diffusion. Various theoretical models exists nowadays to…

Statistical Mechanics · Physics 2024-04-15 Gorka Muñoz-Gil , Guillem Guigó i Corominas , Maciej Lewenstein

Anomalous diffusion is frequently described by scaled Brownian motion (SBM), a Gaussian process with a power-law time dependent diffusion coefficient. Its mean squared displacement is $\langle x^2(t)\rangle\simeq\mathscr{K}(t)t$ with…

Statistical Mechanics · Physics 2014-12-24 J. -H. Jeon , A. V. Chechkin , R. Metzler

Anomalous diffusion processes pose a unique challenge in classification and characterization. Previously (Mangalam et al., 2023, Physical Review Research 5, 023144), we established a framework for understanding anomalous diffusion using…

Adaptation and Self-Organizing Systems · Physics 2024-01-23 Henrik Seckler , Ralf Metzler , Damian G. Kelty-Stephen , Madhur Mangalam

We propose a practical empirical fitting function to characterize the non-Gaussian displacement distribution functions (DispD) often observed for heterogeneous diffusion problems. We first test this fitting function with the problem of a…

Soft Condensed Matter · Physics 2022-07-20 Le Qiao , Nicholas Ilow , Maxime Ignacio , Gary W. Slater

Modern developments in microscopy and image processing are revolutionising areas of physics, chemistry, and biology as nanoscale objects can be tracked with unprecedented accuracy. However, the price paid for having a direct visualisation…

Statistical Mechanics · Physics 2013-03-19 Denis Boyer , David S. Dean , Carlos Mejía-Monasterio , Gleb Oshanin

Anomalous short- and long-time self-diffusion of non-overlapping fractal particles on a percolation cluster with spreading dimension $1.67(2)$ is studied by dynamic Monte Carlo simulations. As reported in Phys. Rev. Lett. 115, 097801…

Computational Physics · Physics 2020-10-08 Marco Heinen

When particles/molecules diffuse in systems that contain obstacles, the steady-state regime (during which the mean-square displacement scales linearly with time, $\left< r^2 \right> \sim t$) is preceded by a transient regime. It is common…

Biological Physics · Physics 2021-08-12 Nicholas Ilow , Gary W. Slater

This article is devoted to the study of the multifractal analysis of ergodic averages in some nonuniformly hyperbolic systems. In particular, our results hold for the robust classes of multidimensional nonuniformly expanding local…

Dynamical Systems · Mathematics 2013-10-10 Xiaoyao Zhou , Ercai Chen

The most common way of estimating the anomalous diffusion exponent from single-particle trajectories consists in a linear fitting of the dependence of the time averaged mean square displacement on the lag time at the log-log scale. However,…

Data Analysis, Statistics and Probability · Physics 2019-01-02 Yann Lanoiselée , Denis S. Grebenkov , Grzegorz Sikora , Aleksandra Grzesiek , Agnieszka Wyłomańska

We investigate the time average mean square displacement $\overline{\delta^2}(x(t))=\int_0^{t-\Delta}[x(t^\prime+\Delta)-x(t^\prime)]^2 dt^\prime/(t-\Delta)$ for fractional Brownian and Langevin motion. Unlike the previously investigated…

Data Analysis, Statistics and Probability · Physics 2014-01-30 Weihua Deng , Eli Barkai
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