# Category: Sensitivity analysis

Applications of sensitivity analysis to environmental sciences
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 app
Variance-based sensitivity analysis
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. Working within a probabilistic framework, it
Elementary effects method
The elementary effects (EE) method is the most used screening method in sensitivity analysis. EE is applied to identify non-influential inputs for a computationally costly mathematical model or for a
Applications of sensitivity analysis in epidemiology
Sensitivity analysis studies the relation between the uncertainty in a model-based the inference and the uncertainties in the model assumptions. Sensitivity analysis can play an important role in epid
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 it
Sensitivity auditing
Sensitivity auditing is an extension of sensitivity analysis for use in policy-relevant modelling studies. Its use is recommended - e.g. in the European Commission Impact assessment guidelines and by
Tornado diagrams, also called tornado plots, tornado charts or butterfly charts, are a special type of Bar chart, where the data categories are listed vertically instead of the standard horizontal pre
Experimental uncertainty analysis
Experimental uncertainty analysis is a technique that analyses a derived quantity, based on the uncertainties in the experimentally measured quantities that are used in some form of mathematical relat
Extreme bounds analysis
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
Applications of sensitivity analysis to model calibration
Sensitivity analysis has important applications in model calibration. One application of sensitivity analysis addresses the question of "What's important to model or system development?" One can seek
Sensitivity analysis of an EnergyPlus model
Sensitivity analysis identifies how uncertainties in input parameters affect important measures of building performance, such as cost, indoor thermal comfort, or CO2 emissions. Input parameters for bu
Applications of sensitivity analysis to business
Sensitivity analysis can be usefully applied to business problem, allowing the identification of those variables which may influence a business decision, such as e.g. an investment. In a decision prob
Fourier amplitude sensitivity testing
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 jo
Applications of sensitivity analysis to multi-criteria decision making
A sensitivity analysis may reveal surprising insights in multi-criteria decision making (MCDM) studies aimed to select the best alternative among a number of competing alternatives. This is an importa
Hyperparameter
In Bayesian statistics, a hyperparameter is a parameter of a prior distribution; the term is used to distinguish them from parameters of the model for the underlying system under analysis. For example