পদ্ধতির তুলনা করুন

	বেয়েশীয় সংবেদনশীলতা বিশ্লেষণ	Markov Model
ক্ষেত্র	অনুকরণ	অনুকরণ
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1984–1994	1906
প্রবর্তক	Berger, J. O. (Bayesian robustness); Saltelli et al. (global SA integration)	Andrei Markov
ধরন	Uncertainty propagation and sensitivity quantification	Probabilistic state-transition model
মৌলিক উৎস	Berger, J. O. (1994). An overview of robust Bayesian analysis. Test, 3(1), 5–124.	Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963
অপর নাম	BSA, Bayesian SA, Bayesian robustness analysis, prior sensitivity analysis	Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process
সম্পর্কিত	5	5
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.	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.