10th EAI International Conference on Performance Evaluation Methodologies and Tools

Research Article

Parametric Sensitivity and Uncertainty Propagation in Dependability Models

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  • @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
Riccardo Pinciroli1,*, Kishor Trivedi2, Andrea Bobbio3
  • 1: Politecnico di Milano
  • 2: Duke University
  • 3: Università del Piemonte Orientale
*Contact email: riccardo.pinciroli@polimi.it

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].

Keywords
epistemic uncertainty propagation parametric sensitivity analysis homogeneous and non-homogeneous markov models hierarchical models railway system dependability
Published
2017-05-03
Publisher
ACM
http://dx.doi.org/10.4108/eai.25-10-2016.2266529
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