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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.
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.
Completed Tornado Diagram. 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 presentation, and the categories are ordered so that the largest bar appears at the top of the chart, the second largest appears second from the top, and so on.
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 to identify important connections between observations, model inputs, and predictions or forecasts.
OpenStudio Analysis Framework and Spreadsheet: [9] A front-end for the OpenStudio Server, allowing for users to create large-scale cloud analyses using OpenStudio measures. SALib: [10] A Python library for general sensitivity analysis, which can be used with user-defined scripts to run EnergyPlus and extract results.
There are several ways to design and conduct a robust Bayes analysis, including the use of (i) parametric conjugate families of distributions, (ii) parametric but non-conjugate families, (iii) density-ratio (bounded density distributions), [11] [12] (iv) ε-contamination, [13] mixture, quantile classes, etc., and (v) bounds on cumulative ...
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. [ 1 ] In a decision problem, the analyst may want to identify cost drivers as well as other quantities for which we need to acquire better knowledge to make an ...
The Nash–Sutcliffe coefficient masks important behaviors that if re-cast can aid in the interpretation of the different sources of model behavior in terms of bias, random, and other components. [11]
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