Kernel-based regression of drift and diffusion coefficients of stochastic processes
From MaRDI portal
Publication:665358
DOI10.1016/j.physleta.2009.07.073zbMath1233.82034OpenAlexW2071889960MaRDI QIDQ665358
David Lamouroux, Klaus Lehnertz
Publication date: 5 March 2012
Published in: Physics Letters. A (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.physleta.2009.07.073
Biomedical imaging and signal processing (92C55) Stochastic methods (Fokker-Planck, Langevin, etc.) applied to problems in time-dependent statistical mechanics (82C31) Interface problems; diffusion-limited aggregation in time-dependent statistical mechanics (82C24)
Related Items (10)
A Direct Method for the Langevin-Analysis of Multidimensional Stochastic Processes with Strong Correlated Measurement Noise ⋮ A robust nonparametric framework for reconstruction of stochastic differential equation models ⋮ Stochastic physics-informed neural ordinary differential equations ⋮ Data-driven inference for stationary jump-diffusion processes with application to membrane voltage fluctuations in pyramidal neurons ⋮ Reconstruction of the modified discrete Langevin equation from persistent time series ⋮ Detrended fluctuation analysis of the Ornstein-Uhlenbeck process: Stationarity versus nonstationarity ⋮ Jump events in the human heartbeat interval fluctuations ⋮ Bi-SOC-states in one-dimensional random cellular automaton ⋮ Enhancing the accuracy of a data-driven reconstruction of bivariate jump-diffusion models with corrections for higher orders of the sampling interval ⋮ Error bounds of the invariant statistics in machine learning of ergodic Itô diffusions
Cites Work
- Unnamed Item
- Unnamed Item
- A survey of numerical methods for stochastic differential equations
- Estimating Kramers-Moyal coefficients in short and non-stationary data sets
- Reconstruction of dynamical equations for traffic flow
- The Fokker-Planck equation. Methods of solutions and applications.
- A note on estimating drift and diffusion parameters from time series
- Nonparametric and semiparametric models.
- Analysis of data sets of stochastic systems
- Deterministic and stochastic features of rhythmic human movement
- On the efficacy of simulated maximum likelihood for estimating the parameters of stochastic differential Equations*
- Deterministic Nonperiodic Flow
- Evidence of Markov properties of high frequency exchange rate data
This page was built for publication: Kernel-based regression of drift and diffusion coefficients of stochastic processes