On sequential Monte Carlo, partial rejection control and approximate Bayesian computation

G. W. Peters, Y. Fan, S. A. Sisson

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


We present a variant of the sequential Monte Carlo sampler by incorporating the partial rejection control mechanism of Liu (2001). We show that the resulting algorithm can be considered as a sequential Monte Carlo sampler with a modified mutation kernel. We prove that the new sampler can reduce the variance of the incremental importance weights when compared with standard sequential Monte Carlo samplers, and provide a central limit theorem. Finally, the sampler is adapted for application under the challenging approximate Bayesian computation modelling framework.
Original languageEnglish
Pages (from-to)1209-1222
Number of pages14
JournalStatistics and Computing
Issue number6
Publication statusPublished - Nov 2012

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