Сравнение на методи

	Устойчиво семплиране на Гибс	Robust Markov Chain Monte Carlo
Област	Бейсови методи	Бейсови методи
Семейство	Bayesian methods	Bayesian methods
Година на възникване	1984–1993	2000s–2010s
Създател	Stuart Geman & Donald Geman (Gibbs sampler, 1984); robustness extensions developed through 1990s Bayesian literature	Roberts, Rosenthal and colleagues; extended by Atchade, Barp, Girolami and others
Тип	Robust MCMC sampler	Bayesian computational sampling
Основополагащ източник	Geweke, J. (1993). Bayesian treatment of the independent Student-t linear model. Journal of Applied Econometrics, 8(S1), S19–S40.	Roberts, G. O. & Rosenthal, J. S. (2004). General state space Markov chains and MCMC algorithms. Probability Surveys, 1, 20–71.
Други названия	robust MCMC Gibbs sampler, outlier-resistant Gibbs sampling, heavy-tailed Gibbs sampler, robust block Gibbs	robust MCMC, outlier-robust MCMC, robust posterior sampling, misspecification-robust MCMC
Свързани	4	5
Резюме	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.	Robust MCMC combines Markov chain Monte Carlo sampling with robustness techniques to produce reliable posterior inference when data contain outliers, when the assumed model is misspecified, or when the target distribution has heavy tails that cause standard samplers to mix poorly or yield distorted estimates.