Sequential model identification with reversible jump ensemble data assimilation method

DOI10.1007/s11222-024-10499-1MaRDI QIDQ6643217

Hai-Xiang Lin, Yue Huan

Publication date: 26 November 2024

Published in: Statistics and Computing (Search for Journal in Brave)

zbMATH Keywords

state-space modelsBayesian inferencedata assimilationmodel identificationRJMCMC

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Inference from stochastic processes and prediction (62M20) Bayesian inference (62F15)

Cites Work

• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Estimation of parameterized spatio-temporal dynamic models
• Model and data reduction for data assimilation: particle filters employing projected forecasts and data with application to a shallow water model
• Identification of physical processes via combined data-driven and data-assimilation methods
• Following a moving target -- Monte Carlo inference for dynamic Bayesian models
• Reversible Jump Particle Filter (RJPF) for Wideband DOA Tracking
• Particle Markov Chain Monte Carlo Methods
• Ensemble Kalman Methods for High-Dimensional Hierarchical Dynamic Space-Time Models
• Dynamic Data Assimilation
• Understanding the Ensemble Kalman Filter
• Ensemble MCMC: accelerating pseudo-marginal MCMC for state space models using the ensemble Kalman filter

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