Linganisha mbinu

	Uchambuzi wa Hisia za Bayesian	Uiguzi wa Monte Carlo
Nyanja	Uigaji	Ufanyaji Maamuzi
Familia	Process / pipeline	MCDM
Mwaka wa asili	1984–1994	1949
Mwanzilishi	Berger, J. O. (Bayesian robustness); Saltelli et al. (global SA integration)	Metropolis, N., Ulam, S.
Aina	Uncertainty propagation and sensitivity quantification	Robustness wrapper — Monte Carlo uncertainty propagation
Chanzo asilia	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
Majina mbadala	BSA, Bayesian SA, Bayesian robustness analysis, prior sensitivity analysis	—
Zinazohusiana	5	0
Muhtasari	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.