Hostname: page-component-76fb5796d-x4r87 Total loading time: 0 Render date: 2024-04-25T10:52:57.009Z Has data issue: false hasContentIssue false
  • English
  • Français

ADVANCES IN CROSS-ENTROPY METHODS

Part of: Inference from stochastic processes Algorithms - Computer Science Probabilistic methods, simulation and stochastic differential equations

Published online by Cambridge University Press:  02 October 2009

THOMAS TAIMRE
THOMAS TAIMRE*
Affiliation:
School of Mathematics and Physics, The University of Queensland, Brisbane 4072, Australia (email: ttaimre@maths.uq.edu.au)
Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'

Keywords

cross-entropyimportance samplingrare eventsoptimization

MSC classification

Secondary: 65C05: Monte Carlo methods 90C27: Combinatorial optimization 68W20: Randomized algorithms 62M05: Markov processes
Type
PhD thesis
Information
Bulletin of the Australian Mathematical Society , Volume 80 , Issue 3 , December 2009 , pp. 521 - 522
Copyright
Copyright © Australian Mathematical Publishing Association Inc. 2009

References

[1]Rubinstein, R. Y., ‘Optimization of computer simulation models with rare events’, European J. Oper. Res. 99 (1997), 89–112.CrossRefGoogle Scholar
[2]Rubinstein, R. Y., ‘The cross-entropy method for combinatorial and continuous optimization’, Methodol. Comput. Appl. Probab. 1 (1999), 127–190.CrossRefGoogle Scholar
[3]Rubinstein, R. Y. and Kroese, D. P., The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation, and Machine Learning (Springer, New York, 2004).CrossRefGoogle Scholar