Global likelihood optimization via the cross-entropy method with an application to mixture models
Zdravko I. Botev, Dirk P. Kroese · 2004
Global likelihood maximization is an important aspect of many statistical analyses. Often the likelihood function is highly multi-extremal. 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. 1