Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates
Mathematics and Computers in Simulation - IMACS sponsored Special issue on the second IMACS seminar on Monte Carlo methods
Uncertainty-Based Information: Elements of Generalized Information Theory
Uncertainty-Based Information: Elements of Generalized Information Theory
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
On the treatment of uncertainties in structural mechanics and analysis
Computers and Structures
Nonspecificity for infinite random sets of indexable type
Fuzzy Sets and Systems
Reliability bounds through random sets: Non-parametric methods and geotechnical applications
Computers and Structures
On solutions of stochastic differential equations with parameters modeled by random sets
International Journal of Approximate Reasoning
A simple mass-action model for the eukaryotic heat shock response and its mathematical validation
Natural Computing: an international journal
Robust Optimization in Simulation: Taguchi and Krige Combined
INFORMS Journal on Computing
Efficient sensitivity analysis in hidden markov models
International Journal of Approximate Reasoning
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This article addresses questions of sensitivity of output values in engineering models with respect to variations in the input parameters. Such an analysis is an important ingredient in the assessment of the safety and reliability of structures. A major challenge in engineering applications lies in the fact that high computational costs have to be faced. Methods have to be developed that admit assertions about the sensitivity of the output with as few computations as possible. This article serves to explore various techniques from precise and imprecise probability theory that may contribute to achieving this goal. It is a case study using an aerospace engineering example and compares sensitivity analysis methods based on random sets, fuzzy sets, interval spreads simulated with the aid of the Cauchy distribution, and sensitivity indices calculated by direct Monte Carlo simulation. Computational cost, accuracy, interpretability, ability to incorporate correlated input and applicability to large scale problems will be discussed.