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WSC
2004
13 years 6 months ago
Global Likelihood Optimization Via the Cross-Entropy Method, with an Application to Mixture Models
Global likelihood maximization is an important aspect of many statistical analyses. Often the likelihood function is highly multi-extremal. This presents a significant challenge t...
Zdravko I. Botev, Dirk P. Kroese
MICCAI
2005
Springer
14 years 5 months ago
Cross Entropy: A New Solver for Markov Random Field Modeling and Applications to Medical Image Segmentation
This paper introduces a novel solver, namely cross entropy (CE), into the MRF theory for medical image segmentation. The solver, which is based on the theory of rare event simulati...
Jue Wu, Albert C. S. Chung
CEC
2007
IEEE
13 years 11 months ago
Bayesian inference in estimation of distribution algorithms
— Metaheuristics such as Estimation of Distribution Algorithms and the Cross-Entropy method use probabilistic modelling and inference to generate candidate solutions in optimizat...
Marcus Gallagher, Ian Wood, Jonathan M. Keith, Geo...
CSDA
2007
108views more  CSDA 2007»
13 years 4 months ago
Nonlinear random effects mixture models: Maximum likelihood estimation via the EM algorithm
Nonlinear random effects models with finite mixture structures are used to identify polymorphism in pharmacokinetic/ pharmacodynamic (PK/PD) phenotypes. An EM algorithm for maxim...
Xiaoning Wang, Alan Schumitzky, David Z. D'Argenio
NIPS
1998
13 years 6 months ago
Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm
We present the CEM (Conditional Expectation Maximization) algorithm as an extension of the EM (Expectation Maximization) algorithm to conditional density estimation under missing ...
Tony Jebara, Alex Pentland