Bayesian methodsBayesian / computational
带测量误差的Metropolis-Hastings算法
带测量误差的Metropolis-Hastings算法是一种贝叶斯马尔可夫链蒙特卡洛（MCMC）方法，当预测变量或结果变量在记录时存在噪声时，它能联合估计模型参数和真实的（未观测到的）协变量值。通过将潜在的真实值视为未知参数，它将测量不确定性完全传播到后验推断中，而不是忽略它或事后进行校正。
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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
- Richardson, S., & Green, P. J. (1997). On Bayesian analysis of mixtures with an unknown number of components. Journal of the Royal Statistical Society: Series B, 59(4), 731-792. DOI: 10.1111/1467-9868.00095 ↗
如何引用本页
ScholarGate. (2026, June 3). Metropolis-Hastings Algorithm for Bayesian Errors-in-Variables Models. ScholarGate. https://scholargate.app/zh/bayesian/metropolis-hastings-with-measurement-error
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- 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 with Measurement Error贝叶斯↔ 比较
- 含测量误差的MCMC贝叶斯↔ 比较
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