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  2. Sensitivity analysis - Wikipedia

    en.wikipedia.org/wiki/Sensitivity_analysis

    Sensitivity analysis. Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be divided and allocated to different sources of uncertainty in its inputs. [1][2] This involves estimating sensitivity indices that quantify the influence of an input or group of inputs on ...

  3. Variance-based sensitivity analysis - Wikipedia

    en.wikipedia.org/wiki/Variance-based_sensitivity...

    Variance-based sensitivity analysis (often referred to as the Sobol’ method or Sobol’ indices, after Ilya M. Sobol’) is a form of global sensitivity analysis. [1][2] Working within a probabilistic framework, it decomposes the variance of the output of the model or system into fractions which can be attributed to inputs or sets of inputs ...

  4. Tornado diagram - Wikipedia

    en.wikipedia.org/wiki/Tornado_diagram

    Tornado diagrams are useful for deterministic sensitivity analysis – comparing the relative importance of variables. For each variable/uncertainty considered, one needs estimates for what the low, base, and high outcomes would be. The sensitive variable is modeled as having an uncertain value while all other variables are held at baseline ...

  5. Morris method - Wikipedia

    en.wikipedia.org/wiki/Morris_method

    Morris method. In applied statistics, the Morris method for global sensitivity analysis is a so-called one-factor-at-a-time method, meaning that in each run only one input parameter is given a new value. It facilitates a global sensitivity analysis by making a number of local changes at different points of the possible range of input values.

  6. Robust Bayesian analysis - Wikipedia

    en.wikipedia.org/wiki/Robust_Bayesian_analysis

    Sensitivity analysis. Robust Bayesian analysis, also called Bayesian sensitivity analysis, investigates the robustness of answers from a Bayesian analysis to uncertainty about the precise details of the analysis. [1][2][3][4][5][6] An answer is robust if it does not depend sensitively on the assumptions and calculation inputs on which it is based.

  7. Elementary effects method - Wikipedia

    en.wikipedia.org/wiki/Elementary_effects_method

    Elementary effects method. Published in 1991 by Max Morris [1] the elementary effects (EE) method[2] is one of the most used [3][4][5][6] screening methods in sensitivity analysis. EE is applied to identify non-influential inputs for a computationally costly mathematical model or for a model with a large number of inputs, where the costs of ...

  8. Sensitivity analysis studies the relationship between the output of a model and its input variables or assumptions. Historically, the need for a role of sensitivity analysis in modelling, and many applications of sensitivity analysis have originated from environmental science and ecology. [1]

  9. Fourier amplitude sensitivity testing - Wikipedia

    en.wikipedia.org/wiki/Fourier_amplitude...

    Fourier amplitude sensitivity testing (FAST) is a variance-based global sensitivity analysis method. The sensitivity value is defined based on conditional variances which indicate the individual or joint effects of the uncertain inputs on the output. FAST first represents conditional variances via coefficients from the multiple Fourier series ...