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Poster Communications<br />
SP1<br />
Wednesday, September 4th<br />
19:45<br />
Ensemble Kalman Filter: An analysis of the<br />
joint estimation of states and parameters<br />
Rafael Oliveira Silva<br />
Márcia D’Elia Branco<br />
USP<br />
The Ensemble Kalman Filter (EnKF) is a sequential Monte Carlo algorithm for inference in linear<br />
and nonlinear state-space models. This filter combined with some other methods propagates the<br />
joint posterior distribution of states and parameters over time. There are fewer papers that consider<br />
the problem of simultaneous state-parameter estimation and existing methods have limitations.<br />
The purpose of this work is to analyze the efficiency of these methods by means of simulation<br />
studies in linear and nonlinear state-space models. The nonlinear estimation problem addressed<br />
here refers to the logistic surplus-production model, for which the EnKF can be considered as a<br />
possible alternative to MCMC algorithms. The simulation results reveal that the accuracy of the<br />
estimates increases when the time series grows, but some parameters present problems in the<br />
estimation.<br />
Keywords: Ensemble Kalman Filter; Joint estimation; Logistic surplus-production model<br />
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