Chapter 2

The Cross-Entropy Method for Estimation

Dirk P. Kroese1, Reuven Y. Rubinstein2 and Peter W. Glynn3,    1School of Mathematics and Physics, The University of Queensland, Brisbane 4072, Australia, 2Faculty of Industrial Engineering and Management, Technion, Haifa, Israel, 3Department of Management Science and Engineering, Stanford University, CA, USA, 1kroese@maths.uq.edu.au2ierrr01@ie.technion.ac.il3glynn@stanford.edu

Abstract

This chapter describes how difficult statistical estimation problems can often be solved efficiently by means of the cross-entropy (CE) method. The CE method can be viewed as an adaptive importance sampling procedure that uses the cross-entropy or Kullback–Leibler divergence as a measure of closeness between two ...

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