方法对比

	Stochastic Sensitivity Analysis	随机马尔可夫模型
领域	仿真	仿真
方法族	Process / pipeline	Process / pipeline
起源年份	1990s–2000s	1993
提出者	Saltelli, A. et al.; Claxton, K. et al. (health economics stream)	Markov, A. A. (probabilistic extension developed by Sonnenberg & Beck and others)
类型	Probabilistic uncertainty quantification technique	Probabilistic state-transition model with Monte Carlo uncertainty propagation
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	Sonnenberg, F. A., & Beck, J. R. (1993). Markov models in medical decision making: A practical guide. Medical Decision Making, 13(4), 322–338.
别名	PSA, Probabilistic Sensitivity Analysis, Stochastic SA, Monte Carlo Sensitivity Analysis	Probabilistic Markov Model, Stochastic Markov Chain, SMM, Monte Carlo Markov Model
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.	A Stochastic Markov Model is a simulation technique that represents a system as a set of mutually exclusive health or decision states, moves a cohort (or individual agents) through those states using probabilistically sampled transition parameters, and aggregates outcomes across thousands of Monte Carlo iterations to produce full probability distributions over costs, outcomes, or rankings rather than single point estimates.