方法对比
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| 贝叶斯敏感性分析× | Stochastic Sensitivity Analysis× | |
|---|---|---|
| 领域 | 仿真 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1984–1994 | 1990s–2000s |
| 提出者≠ | Berger, J. O. (Bayesian robustness); Saltelli et al. (global SA integration) | Saltelli, A. et al.; Claxton, K. et al. (health economics stream) |
| 类型≠ | Uncertainty propagation and sensitivity quantification | Probabilistic uncertainty quantification technique |
| 开创性文献≠ | Berger, J. O. (1994). An overview of robust Bayesian analysis. Test, 3(1), 5–124. DOI ↗ | Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 9780470059975 |
| 别名 | BSA, Bayesian SA, Bayesian robustness analysis, prior sensitivity analysis | PSA, Probabilistic Sensitivity Analysis, Stochastic SA, Monte Carlo Sensitivity Analysis |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | Stochastic Sensitivity Analysis (PSA) extends classical one-at-a-time sensitivity testing by representing uncertain model inputs as probability distributions and propagating them through the model via Monte Carlo sampling. The result is a full distribution of possible outputs, together with rankings of which inputs drive output variance the most — enabling robust, evidence-grounded conclusions under uncertainty. |
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