This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Add to Saved Citations Download to citation manager Add to My Marked Citations Citing Articles Citing Articles via ISI Web of Science (1) Citing Articles via Google Scholar Google Scholar Articles by Botev, Z.I. Articles by Taimre, T. Search for Related Content Social Bookmarking                   What's this?

Generalized Cross-entropy Methods with Applications to Rare-event Simulation and Optimization

Department of Mathematics The University of Queensland Brisbane 4072, Australia botev{at}maths.uq.edu.au

D.P. Kroese

Department of Mathematics The University of Queensland Brisbane 4072, Australia

T. Taimre

Department of Mathematics The University of Queensland Brisbane 4072, Australia

The cross-entropy and minimum cross-entropy methods are well-known Monte Carlo simulation techniques for rare-event probability estimation and optimization. In this paper, we investigate how these methods can be eXtended to provide a general non-parametric cross-entropy framework based on -divergence distance measures. We show how the ² distance, in particular, yields a viable alternative to the Kullback—Leibler distance. The theory is illustrated with various eXamples from density estimation, rare-event simulation and continuous multi-eXtremal optimization.

Key Words: generalized cross-entropy • maXimum entropy method • cross-entropy method • rare-event simulation • stochastic optimization • Csisár's -divergence

CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?