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

被引：0
作者：
G. W. Peters
Y. Fan
S. A. Sisson
机构：
[1] CSIRO Sydney,School of Mathematics and Statistics
[2] University of New South Wales,undefined
来源：
Statistics and Computing | 2012年 / 22卷
关键词：
Approximate Bayesian computation; Bayesian computation; Likelihood-free inference; Partial rejection control; Sequential Monte Carlo samplers;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：1209 / 1222
页数：13
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