Estimating Kramers-Moyal coefficients in short and non-stationary data sets

DOI10.1016/j.physleta.2005.10.066zbMath1234.82014OpenAlexW2155113686MaRDI QIDQ973467

P. J. Beek, A. Daffertshofer, Anke M. van Mourik

Publication date: 2 June 2010

Published in: Physics Letters. A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physleta.2005.10.066

zbMATH Keywords

stochastic processesnon-stationary processesKramers-Moyal expansion

Mathematics Subject Classification ID

Stochastic methods (Fokker-Planck, Langevin, etc.) applied to problems in time-dependent statistical mechanics (82C31)

Related Items (5)

The Kramers-Moyal expansion of the master equation that describes human migration in a bounded domain ⋮ Stochastic physics-informed neural ordinary differential equations ⋮ Data-driven inference for stationary jump-diffusion processes with application to membrane voltage fluctuations in pyramidal neurons ⋮ Kernel-based regression of drift and diffusion coefficients of stochastic processes ⋮ A data-analysis method for identifying differential effects of time-delayed feedback forces and periodic driving forces in stochastic systems

Cites Work

• Statistical properties of a turbulent cascade
• The Fokker-Planck equation. Methods of solution and applications.
• Reconstruction of dynamical equations for traffic flow
• Analysis of data sets of stochastic systems
• Identifying noise sources of time-delayed feedback systems
• Experimental indications for Markov properties of small-scale turbulence
• Diffusion of Periodically Forced Brownian Particles Moving in Space-Periodic Potentials
• Brownian motors: noisy transport far from equilibrium

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