방법 비교

	MCMC with Measurement Error	계층적 베이즈 추론
분야	베이지안	베이지안
계열	Bayesian methods	Bayesian methods
기원 연도	1993	1972 (Lindley & Smith); consolidated 1995–2013
창시자	Richardson & Gilks; Carroll, Ruppert & Stefanski	Lindley & Smith; Gelman et al.
유형	Bayesian computational estimation	Bayesian multilevel model
원전	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	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
별칭	MCMC errors-in-variables, Bayesian measurement error MCMC, MCMC misclassification model, Bayesian errors-in-variables	multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model
관련	6	6
요약	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.	Hierarchical Bayesian inference is a probabilistic modeling framework that organises parameters into levels, placing priors on the group-level parameters and hyperpriors on the parameters governing those priors. It enables partial pooling of information across groups, balancing the extremes of treating each group as independent or merging them into a single estimate.