A new smoothing-regularization approach for a maximum-likelihood estimation problem

DOI10.1007/BF01189476zbMath0801.90089MaRDI QIDQ1322715

Alfredo Noel Iusem, Benar Fux Svaiter

Publication date: 5 May 1994

Published in: Applied Mathematics and Optimization (Search for Journal in Brave)

zbMATH Keywords

positron emission tomographystatistical estimationKullback-Leibler information divergencemaximum-likelihood estimation problemreglarization

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25) Applications of mathematical programming (90C90)

Related Items (1)

A hybrid splitting method for smoothing Tikhonov regularization problem

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Some properties of adding a smoothing step to the EM algorithm
• A relaxed version of Bregman's method for convex programming
• A primal-dual iterative algorithm for a maximum likelihood estimation problem
• On the asymptotic behavior of some alternate smoothing series expansion iterative methods
• Mixture Densities, Maximum Likelihood and the EM Algorithm
• An algorithm for maximizing expected log investment return
• Some continuity properties of polyhedral multifunctions
• Convergence analysis for a multiplicatively relaxed EM algorithm
• A regularized dual-based iterative method for a class of image reconstruction problems
• A Statistical Model for Positron Emission Tomography
• On Perturbations in Systems of Linear Inequalities

This page was built for publication: A new smoothing-regularization approach for a maximum-likelihood estimation problem