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This paper presents a novel approach for constrained state estimation from noisy measurements. The optimal trending algorithms described in this paper assume that the trended system variables have the...
Covariance inflation in the ensemble Kalman filter: a residual nudging perspective and some implications
Covariance inflation ensemble Kalman filter residual nudging perspective some implications
2013/6/17
This note examines the influence of covariance inflation on the distance between the measured observation and the simulated (or predicted) observation with respect to the state estimate. In order for ...
The parameters of temporal models, such as dynamic Bayesian networks, may be modelled in a Bayesian context as static or atemporal variables that influence transition probabilities at every time step....
Data Driven Computing by the Morphing Fast Fourier Transform Ensemble Kalman Filter in Epidemic Spread Simulations
Data Driven Computing Morphing Fast Fourier Transform Ensemble Kalman Filter Epidemic Spread Simulations
2010/3/11
The FFT EnKF data assimilation method is proposed and applied to a stochastic
cell simulation of an epidemic, based on the S-I-R spread model. The FFT EnKF
combines spatial statistics and ensemble f...
OPTIMALITY OF THE AUXILIARY PARTICLE FILTER
Auxiliary particle filter central limit theorem adjustment multiplier weight
2009/9/18
In this article we study asymptotic properties of weighted
samples produced by the auxiliary particle filter (APF) proposed by Pitt
and Shephard [17]. Besides establishing a central limit theorem (C...
On the Convergence of the Ensemble Kalman Filter
Exchangeable random variables Monte-Carlo methods dataassimilation theoretical analysis asymptotics EnKF filtering
2010/3/17
Convergence of the ensemble Kalman filter in the limit for large ensembles
to the Kalman filter is proved. In each step of the filter, convergence of
the ensemble sample covariance follows from a we...