Comparer des méthodes

	Échantillonnage de Gibbs robuste	Échantillonnage de Gibbs
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1984–1993	1984
Auteur d'origine	Stuart Geman & Donald Geman (Gibbs sampler, 1984); robustness extensions developed through 1990s Bayesian literature	Stuart Geman & Donald Geman
Type	Robust MCMC sampler	MCMC sampling algorithm
Source fondatrice	Geweke, J. (1993). Bayesian treatment of the independent Student-t linear model. Journal of Applied Econometrics, 8(S1), S19–S40.	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	robust MCMC Gibbs sampler, outlier-resistant Gibbs sampling, heavy-tailed Gibbs sampler, robust block Gibbs	Gibbs sampler, coordinate-wise MCMC, systematic scan Gibbs, blocked Gibbs sampling
Apparentées	4	5
Résumé	Robust Gibbs sampling is a Markov chain Monte Carlo strategy that pairs the coordinate-wise Gibbs sampler with heavy-tailed or outlier-resistant model specifications — most commonly Student-t likelihoods — so that the posterior inference is not distorted by extreme observations. It achieves robustness through data augmentation: each observation receives a latent variance weight that automatically down-weights outliers during each sampling sweep.	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.