Publisher description for The EM algorithm and extensions / Geoffrey J. McLachlan, Thriyambakam Krishnan.


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This new edition remains the only single source to offer a complete and unified treatment of the theory, methodology, and applications of the EM algorithm. The highly applied area of statistics here outlined involves applications in regression, medical imaging, finite mixture analysis, robust statistical modeling, survival analysis, and repeated-measures designs, among other areas. The text includes newly added and updated results on convergence, and new discussion of categorical data, numerical differentiation, and variants of the EM algorithm. It also explores the relationship between the EM algorithm and the Gibbs sampler and Markov Chain Monte Carlo methods.
With plentiful pedagogical elements-chapter introductions, author and subject indices, exercises, and computer-drawn graphics-this Second Edition of The EM Algorithm and Extensions will prove an essential companion for students and practitioners of advanced statistics.


Library of Congress subject headings for this publication:
Expectation-maximization algorithms.
Estimation theory.
Missing observations (Statistics)