方法对比
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| 贝叶斯情景分析× | 贝叶斯敏感性分析× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2000s | 1984–1994 |
| 提出者≠ | Developed iteratively across Bayesian statistics and scenario planning communities; formalized in risk and decision analysis (Aven, Lempert et al., 2000s) | Berger, J. O. (Bayesian robustness); Saltelli et al. (global SA integration) |
| 类型≠ | Probabilistic hybrid — Bayesian inference integrated with structured scenario analysis | Uncertainty propagation and sensitivity quantification |
| 开创性文献≠ | Aven, T., & Reniers, G. (2013). How to define and interpret a probability in a risk and safety setting. Safety Science, 51(1), 223–231. DOI ↗ | Berger, J. O. (1994). An overview of robust Bayesian analysis. Test, 3(1), 5–124. DOI ↗ |
| 别名 | BSA, Bayesian scenario planning, probabilistic scenario analysis, Bayesian-weighted scenario analysis | BSA, Bayesian SA, Bayesian robustness analysis, prior sensitivity analysis |
| 相关 | 5 | 5 |
| 摘要≠ | Bayesian Scenario Analysis (BSA) combines structured scenario planning with Bayesian probability theory, assigning explicit prior probabilities to alternative futures and updating them as new evidence or expert judgments become available. The result is a probability-weighted distribution of outcomes across scenarios rather than a set of equally-weighted or arbitrarily-weighted futures. | Bayesian Sensitivity Analysis (BSA) combines Bayesian inference with sensitivity analysis to systematically quantify how uncertain model inputs — expressed as prior probability distributions — propagate through a model and influence outputs. It identifies which parameters most drive output variability, supporting robust conclusions under genuine uncertainty. |
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