11. Estimation of Derivatives

  1. Dirk P. Kroese1,
  2. Thomas Taimre1 and
  3. Zdravko I. Botev2

Published Online: 20 SEP 2011

DOI: 10.1002/9781118014967.ch11

Handbook of Monte Carlo Methods

Handbook of Monte Carlo Methods

How to Cite

Kroese, D. P., Taimre, T. and Botev, Z. I. (2011) Estimation of Derivatives, in Handbook of Monte Carlo Methods, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118014967.ch11

Author Information

  1. 1

    University of Queensland

  2. 2

    Université de Montréal

Publication History

  1. Published Online: 20 SEP 2011
  2. Published Print: 28 FEB 2011

ISBN Information

Print ISBN: 9780470177938

Online ISBN: 9781118014967

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Keywords:

  • finite difference method;
  • gradient estimation;
  • infinitesimal perturbation analysis;
  • likelihood ratio method;
  • regenerative process;
  • sensitivity analysis;
  • stochastic optimization

Summary

This chapter discusses four methods for gradient estimation: the finite difference method, infinitesimal perturbation analysis, the likelihood ratio or score function method, and weak derivatives. In addition, it discusses gradient estimation for regenerative processes. The efficient estimation of derivatives is important in sensitivity analysis of simulation output and in stochastic or noisy optimization.

Controlled Vocabulary Terms

likelihood-ratio test; regenerative process; sensitivity analysis; Stochastic optimization; stochastic processes