Bayesian methodsBayesian / computational
含测量误差的MCMC
含测量误差的MCMC方法将马尔可夫链蒙特卡洛抽样应用于贝叶斯模型，这些模型显式地考虑了协变量或结果是带有误差观测的事实。通过将真实的、未观测到的值视为潜在变量，并与所有其他参数一起抽取其联合后验分布，该方法可以校正衰减偏差，即使在某些变量无法精确测量时也能产生有效的推断。
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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 & Hall/CRC. ISBN: 978-1584886334
- Richardson, S. & Gilks, W. R. (1993). A Bayesian approach to measurement error problems in epidemiology using conditional independence models. American Journal of Epidemiology, 138(6), 430-442. link ↗
如何引用本页
ScholarGate. (2026, June 3). Markov Chain Monte Carlo with Measurement Error Models. ScholarGate. https://scholargate.app/zh/bayesian/mcmc-with-measurement-error
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 带有测量误差的贝叶斯推断贝叶斯↔ compare
- Bayesian Regression贝叶斯↔ compare
- Gibbs Sampling贝叶斯↔ compare
- 分层贝叶斯推断贝叶斯↔ compare
- 马尔可夫链蒙特卡洛 (MCMC)贝叶斯↔ compare
- 带测量误差的Metropolis-Hastings算法贝叶斯↔ compare