Compară metode

	Analiza de Sensibilitate Bayesiană	Simulare Monte Carlo
Domeniu	Simulare	Luarea deciziilor
Familie	Process / pipeline	MCDM
Anul apariției	1984–1994	1949
Autorul original	Berger, J. O. (Bayesian robustness); Saltelli et al. (global SA integration)	Metropolis, N., Ulam, S.
Tip	Uncertainty propagation and sensitivity quantification	Robustness wrapper — Monte Carlo uncertainty propagation
Sursa seminală	Berger, J. O. (1994). An overview of robust Bayesian analysis. Test, 3(1), 5–124.	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Denumiri alternative	BSA, Bayesian SA, Bayesian robustness analysis, prior sensitivity analysis	—
Înrudite	5	0
Rezumat	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.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.