方法对比

	Stochastic Sensitivity Analysis	随机离散事件仿真
领域	仿真	仿真
方法族	Process / pipeline	Process / pipeline
起源年份	1990s–2000s	1960s–1970s
提出者	Saltelli, A. et al.; Claxton, K. et al. (health economics stream)	Banks, Carson, Nelson, Nicol; Law, A. M.
类型	Probabilistic uncertainty quantification technique	Stochastic simulation model
开创性文献	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	Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127
别名	PSA, Probabilistic Sensitivity Analysis, Stochastic SA, Monte Carlo Sensitivity Analysis	Stochastic DES, SDES, Probabilistic DES, Monte Carlo DES
相关	5	6
摘要	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.	Stochastic Discrete-Event Simulation (Stochastic DES) models complex systems by advancing simulated time from one discrete event to the next, drawing event durations and inter-arrival times from fitted probability distributions. It is the standard technique for analyzing queues, manufacturing lines, healthcare pathways, and logistics networks under uncertainty, producing output statistics with confidence intervals.