Methoden vergleichen
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| Bayesian Monte Carlo Simulation× | Bayesian Sensitivity Analysis× | |
|---|---|---|
| Fachgebiet | Simulation | Simulation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1987–1990s | 1984–1994 |
| Urheber≠ | O'Hagan, A. and colleagues | Berger, J. O. (Bayesian robustness); Saltelli et al. (global SA integration) |
| Typ≠ | Simulation / uncertainty quantification | Uncertainty propagation and sensitivity quantification |
| Wegweisende Quelle≠ | O'Hagan, A., Buck, C. E., Daneshkhah, A., Eiser, J. R., Garthwaite, P. H., Jenkinson, D. J., Oakley, J. E., & Rakow, T. (2006). Uncertain Judgements: Eliciting Experts' Probabilities. Wiley. ISBN: 9780470029992 | Berger, J. O. (1994). An overview of robust Bayesian analysis. Test, 3(1), 5–124. DOI ↗ |
| Aliasnamen | Bayesian MC, BMC simulation, Bayesian stochastic simulation, Bayesian uncertainty propagation | BSA, Bayesian SA, Bayesian robustness analysis, prior sensitivity analysis |
| Verwandt≠ | 4 | 5 |
| Zusammenfassung≠ | Bayesian Monte Carlo Simulation integrates Bayesian statistical inference with Monte Carlo sampling to propagate uncertainty through complex models. Instead of drawing samples from arbitrary distributions, it conditions sampling on observed data and expert prior knowledge via Bayes' theorem, yielding posterior-based uncertainty estimates that are both statistically coherent and interpretable in probabilistic terms. | 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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