Adaptive Tuning of Hamiltonian Monte Carlo Within Sequential Monte Carlo
Adaptive Tuning of Hamiltonian Monte Carlo Within Sequential Monte Carlo
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DOI：
10.1214/20-ba1222
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发表时间：
2021-09-01
影响因子：
4.4
通讯作者：
Jacob, Pierre E.
中科院分区：
文献类型：
--
作者：
Buchholz, Alexander;Chopin, Nicolas;Jacob, Pierre E.
序贯蒙特卡罗（SMC）采样器是用于贝叶斯计算的MCMC的替代方案。然而，它们的性能在很大程度上取决于用于恢复粒子的马尔可夫核。我们讨论了如何自动校准（使用当前粒子）的哈密顿蒙特卡罗内核SMC。为此，我们建立在Fearnhead和Taylor（2013）的自适应SMC方法的基础上，我们还提出了替代方法。我们通过广泛的数值研究说明了在SMC采样器内使用HMC内核的优点。
Sequential Monte Carlo (SMC) samplers are an alternative to MCMC for Bayesian computation. However, their performance depends strongly on the Markov kernels used to rejuvenate particles. We discuss how to calibrate automatically (using the current particles) Hamiltonian Monte Carlo kernels within SMC. To do so, we build upon the adaptive SMC approach of Fearnhead and Taylor (2013), and we also suggest alternative methods. We illustrate the advantages of using HMC kernels within an SMC sampler via an extensive numerical study.