Learning and filtering via simulation: smoothly jittered particle filters
A key ingredient of many particle filters is the use of the sampling importance resampling algorithm (SIR), which transforms a sample of weighted draws from a prior distribution into equally weighted draws from a posterior distribution.� We give a novel analysis of the SIR algorithm and analyse the jittered generalisation of SIR, showing that existing implementations of jittering lead to marked inferior behaviour over the base SIR algorithm.� We show how jittering can be designed to improve the performance of the SIR algorithm.� We illustrate its performance in practice in the context of three filtering problems.
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