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无掉头采样器 (NUTS)

无掉头采样器 (NUTS) 是 Hoffman 和 Gelman (2014) 提出的一种自适应马尔可夫链蒙特卡洛算法，它通过自动确定跳跃步数来扩展哈密顿蒙特卡洛 (HMC)，消除了最敏感的手动调优参数。NUTS 是 Stan 和 PyMC 中的默认采样器，使得大规模、高维贝叶斯推断在实践中变得可行，而无需用户手动设置轨迹长度。

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无掉头采样器 (NUTS)
Bayesian RegressionHamiltonian Monte Carlo马尔可夫链蒙特卡洛 (MCMC)变分推断

来源

  1. Hoffman, M. D., & Gelman, A. (2014). The No-U-Turn Sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(47), 1593–1623. link ↗
  2. Neal, R. M. (2011). MCMC using Hamiltonian dynamics. In S. Brooks, A. Gelman, G. L. Jones, & X.-L. Meng (Eds.), Handbook of Markov Chain Monte Carlo (pp. 113–162). CRC Press. DOI: 10.1201/b10905-6 ↗
  3. Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1-4398-4095-5

如何引用本页

ScholarGate. (2026, June 3). No-U-Turn Sampler (NUTS). ScholarGate. https://scholargate.app/zh/bayesian/no-u-turn-sampler

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ScholarGate — No-U-Turn Sampler (No-U-Turn Sampler (NUTS)). 于 2026-06-15 检索自 https://scholargate.app/zh/bayesian/no-u-turn-sampler · 数据集： https://doi.org/10.5281/zenodo.20539026