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Everything about Sensitivity analysis

Study of uncertainty in the output of a mathematical model or system

Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system can be divided and allocated to different sources of uncertainty in its inputs. This involves estimating sensitivity indices that quantify the influence of an input or group of inputs on the output. A related practice is uncertainty analysis, which has a greater focus on uncertainty quantification and propagation of uncertainty; ideally, uncertainty and sensitivity analysis should be run in tandem. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Sensitivity analysisA 5-minute orientation
  2. Skim a recent paper: Global sensitivity analysis in epidemiological modelingInfluential work since 2022
  3. Pick up a classic book: Global sensitivity analysisThe most-reprinted book on the subject
  4. Read the landmark paper: Sensitivity Analysis Without AssumptionsCited 514 times
  5. Go sideways: Variance-based sensitivity analysisA closely related topic
  6. Go sideways: Uncertainty quantificationA closely related topic

📖 Definitions

No dictionary entry for this exact phrase.

🗂️ The subject at a glance

  1. Motivation
  2. Mathematical formulation and vocabulary
  3. Challenges, settings and related issues
  4. Sensitivity analysis methods
  5. Complementary research approaches for time-consuming simulations
  6. Related concepts
  7. Sensitivity auditing
  8. Pitfalls and difficulties
  9. SA in international context
  10. Specific applications of sensitivity analysis

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics