7. Computational Methods for Risk and Uncertainty Propagation

  1. Etienne de Rocquigny

Published Online: 11 APR 2012

DOI: 10.1002/9781119969495.ch7

Modelling Under Risk and Uncertainty: An Introduction to Statistical, Phenomenological and Computational Methods

Modelling Under Risk and Uncertainty: An Introduction to Statistical, Phenomenological and Computational Methods

How to Cite

de Rocquigny, E. (2012) Computational Methods for Risk and Uncertainty Propagation, in Modelling Under Risk and Uncertainty: An Introduction to Statistical, Phenomenological and Computational Methods, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9781119969495.ch7

Author Information

  1. Ecole Centrale Paris, Université Paris-Saclay, France

Publication History

  1. Published Online: 11 APR 2012
  2. Published Print: 20 APR 2012

Book Series:

  1. Wiley Series in Probability and Statistics

Book Series Editors:

  1. Walter A. Shewhart and
  2. Samuel S. Wilks

ISBN Information

Print ISBN: 9780470695142

Online ISBN: 9781119969495

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Keywords:

  • computational methods for risk;
  • risk measure computational issues;
  • expectation-based, risk measures;
  • other risk measures;
  • Monte-Carlo simulation;
  • propagation uncertainty control;
  • double-probabilistic, sampling;
  • monotony, regularity and robust risk measure;
  • sensitivity analysis, and ranking;
  • distributed computing

Summary

This chapter contains sections titled:

  • Classifying the risk measure computational issues

  • The generic Monte-Carlo simulation method and associated error control

  • Classical alternatives to direct Monte-Carlo sampling

  • Monotony, regularity and robust risk measure computation

  • Sensitivity analysis and importance ranking

  • Numerical challenges, distributed computing and use of direct or adjoint differentiation of codes

  • Exercises

  • References