Stochastic Sensitivity Analysis — Quantifying Output Uncertainty via Probabilistic Input Sampling

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.