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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 .
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 r {\displaystyle r} of local changes at different points x ( 1 → r ) {\displaystyle x(1 ...
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.
[1] [2] In biomedical engineering, sensitivity analysis can be used to determine system dynamics in ODE-based kinetic models. Parameters corresponding to stages of differentiation can be varied to determine which parameter is most influential on cell fate.
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 the output.
There exist many software tools that can automate sensitivity analysis to various degrees. Here is a non-exhaustive list. Most of these tools have multiple options, including one-at-a-time sensitivity analysis, multidimensional discrete parametric, continuous low-discrepancy distributions, and pareto-front optimization (listed alphabetically):
Please introduce links to this page from ; try the Find link tool for suggestions. ( June 2024 ) In econometrics , extreme bounds analysis is a type of sensitivity analysis which attempts to determine the most extreme possible estimates for a fixed subset of allowed coefficients and a variable set of linear homogeneous restrictions. [ 1 ]
The adjoint state method is a numerical method for efficiently computing the gradient of a function or operator in a numerical optimization problem. [1] It has applications in geophysics, seismic imaging, photonics and more recently in neural networks.