2005/04/05 by Zdravko I. Botev, Dirk P. Kroese · 1 citation
Computer Science · Physics and Astronomy · Mathematics · #Bayesian Methods and Mixture Models #Gaussian Processes and Bayesian Inference #Statistical Mechanics and Entropy #Entropy maximization #Likelihood function #Computer science #Maximization #Entropy (arrow of time) #Cross entropy #Expectation–maximization algorithm #Maximum likelihood #Mixture model #Principle of maximum entropy #Cross-entropy method #Mathematical optimization #Estimation theory #Algorithm #Mathematics #Statistics #Artificial intelligence #Optimization problem
paper · doi:10.1109/wsc.2004.1371358
openalex publication_date 2005/04/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
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.