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Global likelihood optimization via the cross-entropy method with an application to mixture models

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2 Author(s)
Botev, Z. ; Dept. of Math., Queensland Univ., Brisbane, Qld., Australia ; Kroese, D.P.

Global likelihood maximization is an important aspect of many statistical analyses. Often the likelihood function is highly multiextremal. This presents a significant challenge to standard search procedures, which often settle too quickly into an inferior local maximum. We present a new approach based on the cross-entropy (CE) method, and illustrate its use for the analysis of mixture models.

Published in:

Simulation Conference, 2004. Proceedings of the 2004 Winter  (Volume:1 )

Date of Conference:

5-8 Dec. 2004

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