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An algorithm for non-parametric estimation in state-space models
Thi Tuyet Trang Chau, Pierre Ailliot, Valérie Monbet
State-space models are ubiquitous in the statistical literature since they provide a flexible and interpretable framework for analyzing many time series. In most practical applicat…
A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation
Pierre Tandeo, Pierre Ailliot, Marc Bocquet +4
Data assimilation combines forecasts from a numerical model with observations. Most of the current data assimilation algorithms consider the model and observation error terms as ad…
An efficient particle-based method for maximum likelihood estimation in nonlinear state-space models
Thi Tuyet Trang Chau, Pierre Ailliot, Valérie Monbet +1
Data assimilation methods aim at estimating the state of a system by combining observations with a physical model. When sequential data assimilation is considered, the joint distri…