A mean field approximation in data assimilation for nonlinear dynamics

DOI10.1016/j.physd.2004.04.003zbMath1081.82019OpenAlexW2015237862MaRDI QIDQ1886994

Francis J. Alexander, Gregory L. Eyink, Juan M. Restrepo

Publication date: 23 November 2004

Published in: Physica D (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physd.2004.04.003

zbMATH Keywords

Bayes formulaMean fieldData assimilation

Mathematics Subject Classification ID

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

Related Items (8)

A maximum entropy method for particle filtering ⋮ Effective actions for statistical data assimilation ⋮ Numerical Approximation of the Frobenius--Perron Operator using the Finite Volume Method ⋮ Estimating parameters in stochastic systems: A variational Bayesian approach ⋮ A path integral method for data assimilation ⋮ Generalised filtering ⋮ Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions ⋮ Deterministic Mean-Field Ensemble Kalman Filtering

Cites Work

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• The Fokker-Planck equation. Methods of solution and applications
• Discretization methods for one-dimensional Fokker-Planck operators
• Dynamical equations for optimal nonlinear filtering
• Stochastic processes and filtering theory
• Conditional Markov Processes
• Équations du filtrage non linéaire de la prédiction et du lissage
• On the Differential Equations Satisfied by Conditional Probablitity Densities of Markov Processes, with Applications
• Most probable histories for nonlinear dynamics: Tracking climate transitions

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