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| Bayesian Sensitivity Analysis× | Markov-Modell× | |
|---|---|---|
| Fachgebiet | Simulation | Simulation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1984–1994 | 1906 |
| Urheber≠ | Berger, J. O. (Bayesian robustness); Saltelli et al. (global SA integration) | Andrei Markov |
| Typ≠ | Uncertainty propagation and sensitivity quantification | Probabilistic state-transition model |
| Wegweisende Quelle≠ | Berger, J. O. (1994). An overview of robust Bayesian analysis. Test, 3(1), 5–124. DOI ↗ | Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963 |
| Aliasnamen | BSA, Bayesian SA, Bayesian robustness analysis, prior sensitivity analysis | Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | 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. | A Markov Model represents a system as a finite set of states and specifies the probability of moving from one state to another at each time step. By capturing only the current state — not the full history — it enables tractable analysis of complex dynamic processes across health economics, engineering reliability, operations research, and social-science modeling. |
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