20. Model Identification by Utilizing Likelihood-Based Methods

  1. Michael P. H. Stumpf2,
  2. David J. Balding3 and
  3. Mark Girolami4
  1. Andreas Raue and
  2. Jens Timmer

Published Online: 12 SEP 2011

DOI: 10.1002/9781119970606.ch20

Handbook of Statistical Systems Biology

Handbook of Statistical Systems Biology

How to Cite

Raue, A. and Timmer, J. (2011) Model Identification by Utilizing Likelihood-Based Methods, in Handbook of Statistical Systems Biology (eds M. P. H. Stumpf, D. J. Balding and M. Girolami), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9781119970606.ch20

Editor Information

  1. 2

    Division of Molecular Biosciences, Imperial College London, UK

  2. 3

    Institute of Genetics, University College London, UK

  3. 4

    Department of Statistical Science, University College London, UK

Author Information

  1. Institute of Physics and Freiburg Institute for Advanced Studies (FRIAS) and Centre for Biological Systems Analysis (ZBSA), University of Freiburg, Germany

Publication History

  1. Published Online: 12 SEP 2011
  2. Published Print: 21 OCT 2011

ISBN Information

Print ISBN: 9780470710869

Online ISBN: 9781119970606

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Keywords:

  • model identification;
  • likelihood-based model identification strategies;
  • ordinary differential equations (ODEs);
  • confidence intervals;
  • parameter estimation perception;
  • Epo and EpoR interaction model;
  • profile likelihood;
  • mathematical modeling

Summary

This chapter contains sections titled:

  • ODE models for reaction networks

  • Parameter estimation

  • Identifiability

  • The profile likelihood approach

  • Summary

  • Acknowledgements

  • References