Mathematical modeling | Sensitivity analysis
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 important task in decision making. In such a setting each alternative is described in terms of a set of evaluative criteria. These criteria are associated with weights of importance. Intuitively, one may think that the larger the weight for a criterion is, the more critical that criterion should be. However, this may not be the case. It is important to distinguish here the notion of criticality with that of importance. By critical, we mean that a criterion with small change (as a percentage) in its weight, may cause a significant change of the final solution. It is possible criteria with rather small weights of importance (i.e., ones that are not so important in that respect) to be much more critical in a given situation than ones with larger weights. That is, a sensitivity analysis may shed light into issues not anticipated at the beginning of a study. This, in turn, may dramatically improve the effectiveness of the initial study and assist in the successful implementation of the final solution. (Wikipedia).
Overview of various methods for sensitivity analysis in the UQ of subsurface systems
From playlist Uncertainty Quantification
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Sensitivity analysis using classification and regression trees
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Sensitivity Analysis for Financial Models in Excel
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Stefano Marelli: Metamodels for uncertainty quantification and reliability analysis
Abstract: Uncertainty quantification (UQ) in the context of engineering applications aims aims at quantifying the effects of uncertainty in the input parameters of complex models on their output responses. Due to the increased availability of computational power and advanced modelling tech
From playlist Probability and Statistics
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Chapter 12 Sensitivity Specificity Predictive Values Odds Ratios
Ever wandered how to calculate sensitivity, specificity, positive and negative predictive values or odds ratios or even simply what these terms mean? Watch this short lecture.
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From playlist Uncertainty in Climate Change: A Conversation with Climate Scientists and Economists
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For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniq
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