পদ্ধতির তুলনা করুন

	বেয়েশীয় সংবেদনশীলতা বিশ্লেষণ	মন্টে কার্লো সিমুলেশন
ক্ষেত্র	অনুকরণ	সিদ্ধান্ত গ্রহণ
পরিবার	Process / pipeline	MCDM
উদ্ভবের বছর	1984–1994	1949
প্রবর্তক	Berger, J. O. (Bayesian robustness); Saltelli et al. (global SA integration)	Metropolis, N., Ulam, S.
ধরন	Uncertainty propagation and sensitivity quantification	Robustness wrapper — Monte Carlo uncertainty propagation
মৌলিক উৎস	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
অপর নাম	BSA, Bayesian SA, Bayesian robustness analysis, prior sensitivity analysis	—
সম্পর্কিত	5	0
সারসংক্ষেপ	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.