Learning and filtering via simulation: smoothly jittered particle filters
Neil Shephard () and
Thomas Flury
No 469, Economics Series Working Papers from University of Oxford, Department of Economics
Abstract:
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.
Keywords: Importance sampling; Particle filter; Random numbers; Sampling importance resampling; State space models (search for similar items in EconPapers)
JEL-codes: C14 C32 (search for similar items in EconPapers)
Date: 2009-12-01
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-ets
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Citations: View citations in EconPapers (8)
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Persistent link: https://EconPapers.repec.org/RePEc:oxf:wpaper:469
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