Bayesian methodsBayesian / computational
Hamiltonian Monte Carlo with Measurement Error
Hamiltonian Monte Carlo (HMC) 结合测量误差是一种贝叶斯计算策略，用于拟合其中一个或多个协变量被噪声观测到的模型。HMC 从模型参数和未观测到的真实协变量值的后验分布中联合采样，利用基于梯度的提议（proposals）来高效探索高维后验分布，并避免标准 Metropolis 采样缓慢的随机游走行为。
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来源
- Carroll, R. J., Ruppert, D., Stefanski, L. A., & Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models: A Modern Perspective (2nd ed.). Chapman and Hall/CRC. ISBN: 978-1584886334
- Neal, R. M. (2011). MCMC using Hamiltonian dynamics. In S. Brooks, A. Gelman, G. Jones, & X.-L. Meng (Eds.), Handbook of Markov Chain Monte Carlo (pp. 113-162). CRC Press. link ↗
如何引用本页
ScholarGate. (2026, June 3). Hamiltonian Monte Carlo for Bayesian Measurement Error Models. ScholarGate. https://scholargate.app/zh/bayesian/hamiltonian-monte-carlo-with-measurement-error
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将本方法与其最相近的同类并置，并排研读——本馆将书籍铺陈于案上，取舍则由您定夺。
- 带有测量误差的贝叶斯推断贝叶斯↔ 比较
- 含测量误差的Gibbs采样贝叶斯↔ 比较
- Hamiltonian Monte Carlo贝叶斯↔ 比较
- 带测量误差的卡尔曼滤波器贝叶斯↔ 比较
- 含测量误差的MCMC贝叶斯↔ 比较
- 带测量误差的变分推断贝叶斯↔ 比较
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Metropolis-Hastings with measurement errorBayesian Inference with Measurement ErrorMCMC with Measurement ErrorVariational Inference with Measurement ErrorGibbs Sampling with Measurement ErrorHamiltonian Monte Carlo with Missing DataHierarchical Hamiltonian Monte CarloBayesian Model Averaging with Measurement Error