Quantifying uncertainties involves several methods:
1. Sensitivity Analysis: Determines how sensitive the model outputs are to changes in input parameters. 2. Uncertainty Propagation: Propagates input uncertainties through the model to estimate output uncertainties. 3. Monte Carlo Simulations: Uses random sampling to understand the impact of uncertainty in model parameters. 4. Bayesian Methods: Incorporates prior knowledge and updates the probability distribution of model parameters.
