方法对比
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| 稳健贝叶斯推断× | 分层贝叶斯推断× | |
|---|---|---|
| 领域 | 贝叶斯 | 贝叶斯 |
| 方法族 | Bayesian methods | Bayesian methods |
| 起源年份≠ | 1984–1990 | 1972 (Lindley & Smith); consolidated 1995–2013 |
| 提出者≠ | James O. Berger | Lindley & Smith; Gelman et al. |
| 类型≠ | Bayesian sensitivity / robustness framework | Bayesian multilevel model |
| 开创性文献≠ | Berger, J. O. (1990). Robust Bayesian analysis: sensitivity to the prior. Journal of Statistical Planning and Inference, 25(3), 303–328. DOI ↗ | 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 | Bayesian sensitivity analysis, prior robustness, epsilon-contamination Bayesian analysis, robust Bayes | multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model |
| 相关 | 6 | 6 |
| 摘要≠ | 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. | Hierarchical Bayesian inference is a probabilistic modeling framework that organises parameters into levels, placing priors on the group-level parameters and hyperpriors on the parameters governing those priors. It enables partial pooling of information across groups, balancing the extremes of treating each group as independent or merging them into a single estimate. |
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