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

机译:在顺序蒙特卡洛上,部分抑制控制和近似贝叶斯计算

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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.
机译:通过结合Liu(2001)的部分抑制控制机制,我们介绍了顺序蒙特卡洛采样器的一种变体。我们证明了所得算法可被视为具有改进的变异核的顺序蒙特卡洛采样器。我们证明,与标准顺序蒙特卡洛采样器相比,新采样器可以减小重要性增量的方差,并提供一个中心极限定理。最后,采样器适用于具有挑战性的近似贝叶斯计算建模框架。

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