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• W2589491631 abstract "A class of discrete-time branching particle filters is introduced with individual resampling: If there are Nn particles alive at time n, N0=N, an1bn, Ln+1i is the current unnormalized importance weight for particle i and An+1=1Ni=1NnLn+1i, then weight is preserved when Ln+1i(anAn+1,bnAn+1). Otherwise, Ln+1iAn+1+ni offspring are produced and assigned weight An+1, where ni is a Bernoulli of parameter Ln+1iAn+1Ln+1iAn+1. The algorithms are shown to be stable with respect to the number of particles and perform better than the bootstrap algorithm as well as other popular resampled particle filters on both tracking problems considered here. Moreover, the new branching filters run significantly faster than these other particle filters on tracking and Bayesian model selection problems." @default.
• W2589491631 created "2017-03-03" @default.
• W2589491631 creator A5040554923 @default.
• W2589491631 date "2017-07-01" @default.
• W2589491631 modified "2023-09-24" @default.
• W2589491631 title "Residual and stratified branching particle filters" @default.
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• W2589491631 doi "https://doi.org/10.1016/j.csda.2017.02.003" @default.
• W2589491631 hasPublicationYear "2017" @default.
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