Uncertainty and sensitivity analysis
Overview
Uncertainty analyses involve the propagation of uncertainty in model parameters and model structure to obtain confidence statements for the estimate of risk and to identify the model components of dominant importance. Uncertainty analyses are required when there is no a priori knowledge about uncertainty in the risk estimate and when there is a chance that the failure to assess uncertainty may affect the selection of wrong options for risk reduction.
When risk estimates are used for decisionmaking, sensitivity analysis allows the identification of those uncertain input parameters whose uncertainty has the greatest impact on model output uncertainty. It quantifies the relative impact of various sources of uncertainty on the output variables of interest, allowing decisionmakers to assess the utility of further investment into uncertainty reduction.
This submodule is a part of the risk management module.
Learning objectives
Upon completion of this submodule, you should be able to:

Understand different sources and representations of uncertainty

Know how to estimate the relative impact of different sources of uncertainty on a decision

Be able to characterize and quantify the sensitivity of different model parameters on total output uncertainty
Course material
Introduction to sensitivity analysis 

SciPy notebook on sensitivity analysis 

SciPy notebook on the elementary effects method 
In these course materials, applications are presented using the NumPy and SciPy libraries for the Python programming language.
Other resources
We recommend the following sources of further information on this topic:

The opensource OpenTURNS software platform allows sophisticated uncertainty analyses

The opensource Dakota software platform helps analysts and decisionmakers understand outcomes of predictive simulations