方法对比
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| 稳健马尔可夫链蒙特卡洛 (Robust Markov Chain Monte Carlo)× | 稳健贝叶斯推断× | |
|---|---|---|
| 领域 | 贝叶斯 | 贝叶斯 |
| 方法族 | Bayesian methods | Bayesian methods |
| 起源年份≠ | 2000s–2010s | 1984–1990 |
| 提出者≠ | Roberts, Rosenthal and colleagues; extended by Atchade, Barp, Girolami and others | James O. Berger |
| 类型≠ | Bayesian computational sampling | Bayesian sensitivity / robustness framework |
| 开创性文献≠ | Roberts, G. O. & Rosenthal, J. S. (2004). General state space Markov chains and MCMC algorithms. Probability Surveys, 1, 20–71. DOI ↗ | Berger, J. O. (1990). Robust Bayesian analysis: sensitivity to the prior. Journal of Statistical Planning and Inference, 25(3), 303–328. DOI ↗ |
| 别名 | robust MCMC, outlier-robust MCMC, robust posterior sampling, misspecification-robust MCMC | Bayesian sensitivity analysis, prior robustness, epsilon-contamination Bayesian analysis, robust Bayes |
| 相关≠ | 5 | 6 |
| 摘要≠ | 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. | Robust Bayesian inference extends standard Bayesian analysis by replacing a single prior distribution with a class of plausible priors and examining how much the posterior conclusions change across that class. Instead of committing to one prior, the analyst bounds the posterior quantity of interest, revealing whether findings are stable or critically dependent on prior assumptions. |
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