A
Andrea Saltelli
Researcher at Open University of Catalonia
Publications - 205
Citations - 35887
Andrea Saltelli is an academic researcher from Open University of Catalonia. The author has contributed to research in topics: Sensitivity (control systems) & Uncertainty analysis. The author has an hindex of 65, co-authored 184 publications receiving 31540 citations. Previous affiliations of Andrea Saltelli include European Union & Autonomous University of Barcelona.
Papers
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Book
Global Sensitivity Analysis: The Primer
TL;DR: In this article, the authors present a method for setting up Uncertainty and Sensitivity Analyses using Monte Carlo and Linear Regression (MCF) models and a set of experiments.
Report SeriesDOI
Handbook on Constructing Composite Indicators: Methodology and User Guide
Michela Nardo,Michaela Saisana,Andrea Saltelli,Stefano Tarantola,Anders Hoffman,Enrico Giovannini +5 more
TL;DR: In this paper, the authors present a handbook for constructing and using composite indicators for policy makers, academics, the media and other interested parties, which is concerned with those which compare and rank country performance in areas such as industrial competitiveness, sustainable development, globalisation and innovation.
Book
Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models
TL;DR: In this paper, the authors present a method for sensitivity analysis of a fish population model using Monte Carlo filtering and variance-based methods, which is based on the Bayesian uncertainty estimation.
Journal ArticleDOI
Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index
Andrea Saltelli,Paola Annoni,Ivano Azzini,Francesca Campolongo,Marco Ratto,Stefano Tarantola +5 more
TL;DR: Existing and new practices for sensitivity analysis of model output are compared and recommendations on which to use are offered to help practitioners choose which techniques to use.
Journal ArticleDOI
Making best use of model evaluations to compute sensitivity indices
TL;DR: In this paper, the same set of model evaluations can be used to compute double estimates of the total effect of two factors taken together, for all such k 2 couples, where k is the dimensionality of the model.