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Everything about Variance-based sensitivity analysis
Form of global sensitivity analysis
Variance-based sensitivity analysis is a form of global sensitivity analysis. 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. For example, given a model with two inputs and one output, one might find that 70% of the output variance is caused by the variance in the first input, 20% by the variance in the second, and 10% due to interactions between the two. These percentages are directly interpreted as measures of sensitivity. Variance-based measures of sensitivity are attractive because they measure sensitivity across the whole input space, they can deal with nonlinear responses, and they can measure the effect of interactions in non-additive systems. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Variance-based sensitivity analysisA 5-minute orientation
- Skim a recent paper: Variance-based global sensitivity analysis for rear-end crash investigation using deep learningInfluential work since 2022
- Pick up a classic book: NanosystemsThe most-reprinted book on the subject
- Read the landmark paper: Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity indexCited 3,417 times
- Go sideways: Fourier amplitude sensitivity testingA closely related topic
- Go sideways: Gauss–Markov theoremA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index
Analytical Variance-Based Global Sensitivity Analysis in Simulation-Based Design Under Uncertainty
A new pruning heuristic based on variance analysis of sensitivity information
Sensitivity analysis of building energy performance: A simulation-based approach using OFAT and variance-based sensitivity analysis methods
An effective approximation for variance-based global sensitivity analysis
Variance-based sensitivity analysis of a forest growth model
Variance-Based Global Sensitivity Analysis for Power Systems
Variance-based sensitivity analysis for time-dependent processes
🆕 Influential since 2022
Variance-based global sensitivity analysis for rear-end crash investigation using deep learning
Red light crossing violations modelling using deep learning and variance-based sensitivity analysis
Modeling green recycled aggregate concrete using machine learning and variance-based sensitivity analysis
Introducing variance-based global sensitivity analysis for uncertainty enabled operational and economic aircraft technology assessment
Variance-based global sensitivity analysis of a hybrid thermoelectric generator fuzzy system
📚 Books worth reading
NanosystemsK. Eric Drexler · 1992
Small Area StatisticsRichard Platek, R. Platek · 1987
Business Analytics, Global EditionEvans, James R. · 1920
Sensitivity AnalysisAndrea Saltelli, K. Chan · 2000
CIMA revision cardsBob Scarlett · 2009
Radiation detection and measurementGlenn F. Knoll · 2010
Mathematical programming for agricultural, environmental, and resource economicsHarry Mason Kaiser · 2010
Digital Signal Processing · 2006
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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