विधियों की तुलना करें

	बायेसियन संवेदनशीलता विश्लेषण	बायेसियन मार्कोव मॉडल
क्षेत्र	अनुकरण	अनुकरण
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1984–1994	1990s–2000s
प्रवर्तक	Berger, J. O. (Bayesian robustness); Saltelli et al. (global SA integration)	Briggs, A.; Sculpher, M.; and broader Bayesian statistics community
प्रकार	Uncertainty propagation and sensitivity quantification	Probabilistic state-transition simulation
मौलिक स्रोत	Berger, J. O. (1994). An overview of robust Bayesian analysis. Test, 3(1), 5–124.	Briggs, A., Sculpher, M., Claxton, K. (2006). Decision Modelling for Health Economic Evaluation. Oxford University Press, Oxford. ISBN: 9780198526629
उपनाम	BSA, Bayesian SA, Bayesian robustness analysis, prior sensitivity analysis	Bayesian Markov Chain Model, Bayesian State-Transition Model, BMM, Bayesian Cohort Simulation
संबंधित	5	4
सारांश	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 Bayesian Markov model is a state-transition simulation method that combines Markov chain cohort modeling with Bayesian statistical inference. By placing prior distributions on transition probabilities and updating them with observed data, the approach propagates full parameter uncertainty through the simulation, yielding posterior distributions over outcomes such as costs, life-years, or quality-adjusted life-years rather than single-point estimates.