Сравнение на методи

	Байесов анализ на чувствителността	Стохастичен анализ на чувствителността
Област	Симулационно моделиране	Симулационно моделиране
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1984–1994	1990s–2000s
Създател	Berger, J. O. (Bayesian robustness); Saltelli et al. (global SA integration)	Saltelli, A. et al.; Claxton, K. et al. (health economics stream)
Тип	Uncertainty propagation and sensitivity quantification	Probabilistic uncertainty quantification technique
Основополагащ източник	Berger, J. O. (1994). An overview of robust Bayesian analysis. Test, 3(1), 5–124.	Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 9780470059975
Други названия	BSA, Bayesian SA, Bayesian robustness analysis, prior sensitivity analysis	PSA, Probabilistic Sensitivity Analysis, Stochastic SA, Monte Carlo Sensitivity Analysis
Свързани	5	5
Резюме	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.	Stochastic Sensitivity Analysis (PSA) extends classical one-at-a-time sensitivity testing by representing uncertain model inputs as probability distributions and propagating them through the model via Monte Carlo sampling. The result is a full distribution of possible outputs, together with rankings of which inputs drive output variance the most — enabling robust, evidence-grounded conclusions under uncertainty.