方法证据记录
Bayesian Sensitivity Analysis
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Bayesian Sensitivity Analysis — Prior-informed uncertainty propagation and output sensitivity assessment
分类方法记录 · process-pipeline / simulation
- Berger, J. O. (1994). An overview of robust Bayesian analysis. Test, 3(1), 5–124. · DOI 10.1007/BF02562676
- 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
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