Comparer des méthodes

	Échantillonnage de Gibbs avec erreur de mesure	Échantillonnage de Gibbs
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1990–1993	1984
Auteur d'origine	Gelfand & Smith (Gibbs sampler); Richardson & Gilks (measurement error extension)	Stuart Geman & Donald Geman
Type	Bayesian MCMC sampling algorithm	MCMC sampling algorithm
Source fondatrice	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.	Geman, S. & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), 721-741.
Alias	Gibbs sampler with errors-in-variables, MCMC measurement error model, Bayesian errors-in-variables Gibbs, Gibbs EIV sampling	Gibbs sampler, coordinate-wise MCMC, systematic scan Gibbs, blocked Gibbs sampling
Apparentées	5	5
Résumé	Gibbs sampling with measurement error is a Bayesian MCMC method that jointly estimates unknown true covariate values and model parameters when the observed data are corrupted by measurement error. By treating the latent true values as additional unknowns, it samples all quantities iteratively from their full conditional distributions, propagating measurement uncertainty into every downstream inference.	Gibbs sampling is a Markov chain Monte Carlo algorithm that approximates a high-dimensional posterior distribution by repeatedly drawing each parameter from its full conditional distribution given all other parameters and the data. Because each draw is exact from a conditional — not a proposal that may be rejected — the sampler is efficient when those conditionals are available in closed form.