State and parameter estimation in stochastic dynamical models

DOI10.1016/j.physd.2010.06.001zbMath1197.37100OpenAlexW2124049286MaRDI QIDQ1957120

Timothy DelSole, Xiao-Song Yang

Publication date: 24 September 2010

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

Full work available at URL: https://doi.org/10.1016/j.physd.2010.06.001

zbMATH Keywords

maximum likelihooddata assimilationensemble Kalman filterstochastic parameter estimation

Mathematics Subject Classification ID

Point estimation (62F10) Filtering in stochastic control theory (93E11) Simulation of dynamical systems (37M05)

Related Items (9)

Multi-Resolution Filters for Massive Spatio-Temporal Data ⋮ Autodifferentiable Ensemble Kalman Filters ⋮ A Stochastic Approximation-Langevinized Ensemble Kalman Filter Algorithm for State Space Models with Unknown Parameters ⋮ A bimodality trap in model projections ⋮ Data assimilation methods for neuronal state and parameter estimation ⋮ Ensemble Kalman Methods for High-Dimensional Hierarchical Dynamic Space-Time Models ⋮ Estimating parameters in stochastic systems: A variational Bayesian approach ⋮ A shadowing-based inflation scheme for ensemble data assimilation ⋮ Kalman filter data assimilation: Targeting observations and parameter estimation

Cites Work

• Unnamed Item
• Stochastic models, estimation, and control. Vol. 2,3
• Statistical decision theory and Bayesian analysis. 2nd ed
• On stochastic parameter estimation using data assimilation
• Stochastic processes and filtering theory
• Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model
• Handbook of stochastic methods for physics, chemistry and the natural sciences.

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