Research Article
Parametric Sensitivity and Uncertainty Propagation in Dependability Models
@INPROCEEDINGS{10.4108/eai.25-10-2016.2266529, author={Riccardo Pinciroli and Kishor Trivedi and Andrea Bobbio}, title={Parametric Sensitivity and Uncertainty Propagation in Dependability Models}, proceedings={10th EAI International Conference on Performance Evaluation Methodologies and Tools}, publisher={ACM}, proceedings_a={VALUETOOLS}, year={2017}, month={5}, keywords={epistemic uncertainty propagation parametric sensitivity analysis homogeneous and non-homogeneous markov models hierarchical models railway system dependability}, doi={10.4108/eai.25-10-2016.2266529} }- Riccardo Pinciroli
Kishor Trivedi
Andrea Bobbio
Year: 2017
Parametric Sensitivity and Uncertainty Propagation in Dependability Models
VALUETOOLS
ACM
DOI: 10.4108/eai.25-10-2016.2266529
Abstract
Input parameters of dependability models are often not known accurately. Two principal methods of dealing with such parametric uncertainty are: sensitivity analysis and uncertainty propagation. This paper is an initial attempt to link the two approaches. The case-study used here (i.e., the multi-voltage propulsion system for the Italian High Speed Railway) also enhances the model presented in [6].
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