方法对比

	鲁棒吉布斯采样	Bayesian Regression
领域	贝叶斯	贝叶斯
方法族	Bayesian methods	Bayesian methods
起源年份	1984–1993	—
提出者	Stuart Geman & Donald Geman (Gibbs sampler, 1984); robustness extensions developed through 1990s Bayesian literature	—
类型	Robust MCMC sampler	Bayesian linear model
开创性文献	Geweke, J. (1993). Bayesian treatment of the independent Student-t linear model. Journal of Applied Econometrics, 8(S1), S19–S40.	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
别名	robust MCMC Gibbs sampler, outlier-resistant Gibbs sampling, heavy-tailed Gibbs sampler, robust block Gibbs	bayesian linear regression, probabilistic regression, bayesian regresyon
相关	4	2
摘要	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.	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.