Methoden vergleichen

	Stochastic Sensitivity Analysis	Stochastisches Markov-Modell
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1990s–2000s	1993
Urheber	Saltelli, A. et al.; Claxton, K. et al. (health economics stream)	Markov, A. A. (probabilistic extension developed by Sonnenberg & Beck and others)
Typ	Probabilistic uncertainty quantification technique	Probabilistic state-transition model with Monte Carlo uncertainty propagation
Wegweisende Quelle	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.
Aliasnamen	PSA, Probabilistic Sensitivity Analysis, Stochastic SA, Monte Carlo Sensitivity Analysis	Probabilistic Markov Model, Stochastic Markov Chain, SMM, Monte Carlo Markov Model
Verwandt	5	6
Zusammenfassung	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.