6. Combined Model Estimation through Inverse Techniques

  1. Etienne de Rocquigny

Published Online: 11 APR 2012

DOI: 10.1002/9781119969495.ch6

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) Combined Model Estimation through Inverse Techniques, in Modelling Under Risk and Uncertainty: An Introduction to Statistical, Phenomenological and Computational Methods, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9781119969495.ch6

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:

  • combined model estimation;
  • inverse techniques;
  • handling calibration data;
  • key distinctions, between algorithms;
  • gradual inverse algorithms;
  • general estimation problem;
  • handling residuals, model uncertainty;
  • importance factors, for accuracy;
  • intrinsic variability identification;
  • moment methods

Summary

This chapter contains sections titled:

  • Introducing inverse techniques

  • One-dimensional introduction of the gradual inverse algorithms

  • The general structure of inverse algorithms: Residuals, identifiability, estimators, sensitivity and epistemic uncertainty

  • Specificities for parameter identification, calibration or data assimilation algorithms

  • Intrinsic variability identification

  • Conclusion: The modelling process and open statistical and computing challenges

  • Exercises

  • References