Global sensitivity analysis

Definition

Global sensitivity analysis is a technique used to determine how the variability in the output of a model can be attributed to different sources of uncertainty in its inputs. This method evaluates the influence of each input parameter on the model's output over its entire range, rather than just around a nominal point. This approach is crucial in understanding which parameters are most impactful, helping to prioritize efforts in refining models and improving predictions.

5 Must Know Facts For Your Next Test

1. Global sensitivity analysis helps identify which parameters most significantly affect model outputs, allowing for better resource allocation in model refinement.
2. Unlike local sensitivity analysis, global sensitivity analysis considers the full range of input parameters, capturing interactions and non-linear effects.
3. It often employs methods such as variance-based approaches, such as Sobol indices, to quantify the contribution of each input variable.
4. This analysis can be particularly useful in fields like environmental modeling, engineering, and finance where understanding uncertainty is critical.
5. By identifying sensitive parameters, global sensitivity analysis assists in risk management and decision-making processes.

Review Questions

• How does global sensitivity analysis differ from local sensitivity analysis in terms of evaluating input variability?
  • Global sensitivity analysis assesses the influence of input parameters over their entire range, allowing for the identification of interactions and non-linear effects. In contrast, local sensitivity analysis only examines how small changes around a specific point affect the output. This means global sensitivity analysis provides a more comprehensive understanding of how different inputs contribute to uncertainty in model predictions.
• What are some common methods used in global sensitivity analysis to quantify the influence of input parameters on model outputs?
  • Common methods include variance-based approaches like Sobol indices, which partition the total variance of the output into contributions from each input variable. Other techniques may involve elementary effects or regression-based methods to gauge parameter importance. These methodologies help quantify how variations in inputs impact overall model predictions, facilitating better risk assessment and decision-making.
• Evaluate the role of global sensitivity analysis in improving model reliability and informing decision-making processes across various fields.
  • Global sensitivity analysis plays a critical role in enhancing model reliability by pinpointing which input variables significantly influence outcomes. This knowledge allows practitioners to focus their efforts on refining those parameters that matter most, ultimately leading to more accurate predictions. In fields like environmental modeling or finance, understanding uncertainty helps inform strategic decisions and mitigate risks, making global sensitivity analysis an essential tool for effective decision-making.

Related terms

Monte Carlo Simulation:

A statistical method used to model and analyze complex systems by generating random samples from input distributions to predict a range of possible outcomes.

Uncertainty Quantification: The process of quantifying uncertainties in model predictions due to variabilities in input parameters and assumptions.

Local Sensitivity Analysis: A technique that assesses how small changes in input parameters affect the output of a model, typically evaluated around a specific point or baseline.