Bandingkan kaedah

	MCMC dengan Ralat Pengukuran	Inferensi Bayesian Hierarki
Bidang	Bayesian	Bayesian
Keluarga	Bayesian methods	Bayesian methods
Tahun asal	1993	1972 (Lindley & Smith); consolidated 1995–2013
Pengasas	Richardson & Gilks; Carroll, Ruppert & Stefanski	Lindley & Smith; Gelman et al.
Jenis	Bayesian computational estimation	Bayesian multilevel model
Sumber perintis	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
Alias	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
Berkaitan	6	6
Ringkasan	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.