方法对比

	带测量误差的贝叶斯网络	含测量误差的MCMC
领域	贝叶斯	贝叶斯
方法族	Bayesian methods	Bayesian methods
起源年份	1988 (Bayesian networks); measurement-error extension: 1990s	1993
提出者	Judea Pearl (Bayesian networks); measurement-error extension developed in epidemiology and psychometrics through the 1990s–2000s	Richardson & Gilks; Carroll, Ruppert & Stefanski
类型	Probabilistic graphical model with latent variables	Bayesian computational estimation
开创性文献	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797	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
别名	BN-ME, errors-in-variables Bayesian network, Bayesian graphical model with measurement error, latent variable Bayesian network	MCMC errors-in-variables, Bayesian measurement error MCMC, MCMC misclassification model, Bayesian errors-in-variables
相关	5	6
摘要	A Bayesian network with measurement error is a probabilistic directed acyclic graphical model in which one or more node variables are observed with error rather than exactly. Latent true-value nodes are introduced for mismeasured variables, and the model jointly infers the network's conditional probability parameters and the unobserved true values from the noisy observations.	MCMC with measurement error applies Markov chain Monte Carlo sampling to Bayesian models that explicitly account for the fact that covariates or outcomes are observed with error. By treating the true, unobserved values as latent variables and sampling their joint posterior alongside all other parameters, the method corrects for attenuation bias and produces valid inference even when some variables cannot be measured exactly.