Salīdzināt metodes

	Beijesa scenāriju analīze	Beiešiskā jutīguma analīze
Nozare	Simulācija	Simulācija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2000s	1984–1994
Autors	Developed iteratively across Bayesian statistics and scenario planning communities; formalized in risk and decision analysis (Aven, Lempert et al., 2000s)	Berger, J. O. (Bayesian robustness); Saltelli et al. (global SA integration)
Tips	Probabilistic hybrid — Bayesian inference integrated with structured scenario analysis	Uncertainty propagation and sensitivity quantification
Pirmavots	Aven, T., & Reniers, G. (2013). How to define and interpret a probability in a risk and safety setting. Safety Science, 51(1), 223–231.	Berger, J. O. (1994). An overview of robust Bayesian analysis. Test, 3(1), 5–124.
Citi nosaukumi	BSA, Bayesian scenario planning, probabilistic scenario analysis, Bayesian-weighted scenario analysis	BSA, Bayesian SA, Bayesian robustness analysis, prior sensitivity analysis
Saistītās	5	5
Kopsavilkums	Bayesian Scenario Analysis (BSA) combines structured scenario planning with Bayesian probability theory, assigning explicit prior probabilities to alternative futures and updating them as new evidence or expert judgments become available. The result is a probability-weighted distribution of outcomes across scenarios rather than a set of equally-weighted or arbitrarily-weighted futures.	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.