Linganisha mbinu

	Uchambuzi wa Usikivu wa Matukio ya Sera	Uchambuzi wa Hisia za Kimahesabu
Nyanja	Uigaji	Uigaji
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1990s–2000s	1990s–2000s
Mwanzilishi	Saltelli, A. et al.; Lempert, R. J. et al.	Saltelli, A. et al.; Claxton, K. et al. (health economics stream)
Aina	Analytical framework combining scenario planning with sensitivity analysis	Probabilistic uncertainty quantification technique
Chanzo asilia	Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. John Wiley & Sons, Chichester. ISBN: 9780470059975	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
Majina mbadala	PSSA, Policy Sensitivity Analysis, Scenario-Based Sensitivity Analysis, Policy Robustness Analysis	PSA, Probabilistic Sensitivity Analysis, Stochastic SA, Monte Carlo Sensitivity Analysis
Zinazohusiana	5	5
Muhtasari	Policy Scenario Sensitivity Analysis (PSSA) combines structured scenario planning with formal sensitivity analysis to determine which model inputs and policy parameters most strongly drive outcomes across a set of distinct policy alternatives or future states. It is widely used in public health, climate, energy, and economic policy modeling to identify robust interventions that perform well even when key assumptions vary.	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.