序列蒙特卡罗中的哈密顿蒙特卡罗自适应调谐

arXiv: Computation Pub Date : 2018-08-23 DOI:10.1214/20-ba1222

Alexander Buchholz, N. Chopin, P. Jacob

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引用次数: 21

摘要

时序蒙特卡罗(SMC)采样器是贝叶斯计算的一个有吸引力的替代方案。然而，它们的性能在很大程度上取决于用于再生粒子的马尔可夫核。我们讨论了如何在SMC内(使用当前粒子)自动校准哈密顿蒙特卡罗核。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采样器中使用HMC核的优点。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Adaptive Tuning Of Hamiltonian Monte Carlo Within Sequential Monte Carlo

Sequential Monte Carlo (SMC) samplers form an attractive alternative to MCMC for Bayesian computation. However, their performance depends strongly on the Markov kernels used to re- juvenate 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 ap- proach 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.

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来源期刊

arXiv: Computation

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