Bayesian methodsBayesian / computational
含测量误差的Gibbs采样
含测量误差的Gibbs采样是一种贝叶斯MCMC方法，在观测数据受到测量误差干扰时，能够联合估计未知的真实协变量值和模型参数。通过将潜在的真实值视为额外的未知量，它迭代地从所有量的全条件分布中进行采样，将测量不确定性传播到所有下游推断中。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Gelfand, A. E. & Smith, A. F. M. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85(410), 398–409. DOI: 10.1080/01621459.1990.10476213 ↗
- 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. DOI: 10.1093/oxfordjournals.aje.a116875 ↗
如何引用本页
ScholarGate. (2026, June 3). Gibbs Sampling for Models with Measurement Error. ScholarGate. https://scholargate.app/zh/bayesian/gibbs-sampling-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
- Gibbs Sampling贝叶斯↔ compare
- Hamiltonian Monte Carlo with Measurement Error贝叶斯↔ compare
- 含测量误差的MCMC贝叶斯↔ compare
- 带测量误差的Metropolis-Hastings算法贝叶斯↔ compare