Salīdzināt metodes

	Stochastic Sensitivity Analysis	Stohastiskais Markova modelis
Nozare	Simulācija	Simulācija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1990s–2000s	1993
Autors	Saltelli, A. et al.; Claxton, K. et al. (health economics stream)	Markov, A. A. (probabilistic extension developed by Sonnenberg & Beck and others)
Tips	Probabilistic uncertainty quantification technique	Probabilistic state-transition model with Monte Carlo uncertainty propagation
Pirmavots	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.
Citi nosaukumi	PSA, Probabilistic Sensitivity Analysis, Stochastic SA, Monte Carlo Sensitivity Analysis	Probabilistic Markov Model, Stochastic Markov Chain, SMM, Monte Carlo Markov Model
Saistītās	5	6
Kopsavilkums	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.