Modeling Dependence and Assessing the Effect of Uncertainty in Dependence in Probabilistic Analysis and Decision Under Uncertainty

Paper #:
  • 2010-01-0697

Published:
  • 2010-04-12
Citation:
Nikolaidis, E. and Mourelatos, Z., "Modeling Dependence and Assessing the Effect of Uncertainty in Dependence in Probabilistic Analysis and Decision Under Uncertainty," SAE Technical Paper 2010-01-0697, 2010, https://doi.org/10.4271/2010-01-0697.
Pages:
7
Abstract:
A complete probabilistic model of uncertainty in probabilistic analysis and design problems is the joint probability distribution of the random variables. Often, it is impractical to estimate this joint probability distribution because the mechanism of the dependence of the variables is not completely understood. This paper proposes modeling dependence by using copulas and demonstrates their representational power. It also compares this representation with a Monte-Carlo simulation using dispersive sampling.
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