Bayesian Sensitivity Analysis — Prior-informed uncertainty propagation and output sensitivity assessment
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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Method map
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Izvori
- 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
Kako citirati ovu stranicu
ScholarGate. (2026, June 3). Bayesian Sensitivity Analysis — Prior-informed uncertainty propagation and output sensitivity assessment. ScholarGate. https://scholargate.app/hr/simulation/bayesian-sensitivity-analysis
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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