Robust Gibbs Sampling
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.
Rekodi ya chanzo
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- Geweke, J. (1993). Bayesian treatment of the independent Student-t linear model. Journal of Applied Econometrics, 8(S1), S19–S40. · DOI 10.1002/jae.3950080504
- Chib, S. & Greenberg, E. (1995). Understanding the Metropolis-Hastings algorithm. The American Statistician, 49(4), 327–335. · DOI 10.1080/00031305.1995.10476177
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