方法对比
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| 鲁棒变分推断× | 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≠ | 2008-2018 | 1984–1990 |
| 提出者≠ | Fujisawa & Eguchi (2008); Futami, Sato & Sugiyama (2018) | James O. Berger |
| 类型≠ | Robust approximate Bayesian inference | Bayesian sensitivity / robustness framework |
| 开创性文献≠ | Futami, F., Sato, I. & Sugiyama, M. (2018). Variational inference based on robust divergences. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 84:813-822. link ↗ | Berger, J. O. (1990). Robust Bayesian analysis: sensitivity to the prior. Journal of Statistical Planning and Inference, 25(3), 303–328. DOI ↗ |
| 别名 | RVI, robust VI, outlier-robust variational Bayes, power-divergence variational inference | Bayesian sensitivity analysis, prior robustness, epsilon-contamination Bayesian analysis, robust Bayes |
| 相关 | 6 | 6 |
| 摘要≠ | Robust variational inference (RVI) extends standard variational inference by replacing the Kullback-Leibler divergence with a divergence measure that is less sensitive to outliers and model misspecification — such as the beta-divergence or a Renyi-type divergence. This yields posterior approximations that remain well-behaved even when a fraction of the data departs from the assumed model. | 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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