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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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