Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Robust Gibbs Sampling× | Markov Chain Monte Carlo Robusto× | |
|---|---|---|
| Área | Bayesiano | Bayesiano |
| Família | Bayesian methods | Bayesian methods |
| Ano de origem≠ | 1984–1993 | 2000s–2010s |
| Autor original≠ | 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 |
| Tipo≠ | Robust MCMC sampler | Bayesian computational sampling |
| Fonte seminal≠ | Geweke, J. (1993). Bayesian treatment of the independent Student-t linear model. Journal of Applied Econometrics, 8(S1), S19–S40. DOI ↗ | Roberts, G. O. & Rosenthal, J. S. (2004). General state space Markov chains and MCMC algorithms. Probability Surveys, 1, 20–71. DOI ↗ |
| Outros nomes | 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 |
| Relacionados≠ | 4 | 5 |
| Resumo≠ | 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. |
| ScholarGateConjunto de dados ↗ |
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