There are several methods used in sensitivity analysis, each with its own strengths and weaknesses. Common methods include:
1. Local Sensitivity Analysis: Examines the effect of small changes in input variables around a specific point. 2. Global Sensitivity Analysis: Considers the entire range of input variables to determine their overall impact on the model's output. 3. One-at-a-Time (OAT) Sensitivity Analysis: Changes one input variable at a time while keeping others constant to observe its effect on the output. 4. Variance-Based Methods: Decompose the variance of the output to attribute it to different input variables.
