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The only single-source----now completely updated andrevised----to offer a unified treatment of the theory,methodology, and applications of the EM algorithm Complete with updates that capture developments from the pastdecade, The EM Algorithm and Extensions, Second Editionsuccessfully provides a basic understanding of the EM algorithm bydescribing its inception, implementation, and applicability innumerous statistical contexts. In conjunction with the fundamentalsof the topic, the authors discuss convergence issues andcomputation of standard errors, and, in addition, unveil manyparallels and…mehr

Produktbeschreibung
The only single-source----now completely updated andrevised----to offer a unified treatment of the theory,methodology, and applications of the EM algorithm Complete with updates that capture developments from the pastdecade, The EM Algorithm and Extensions, Second Editionsuccessfully provides a basic understanding of the EM algorithm bydescribing its inception, implementation, and applicability innumerous statistical contexts. In conjunction with the fundamentalsof the topic, the authors discuss convergence issues andcomputation of standard errors, and, in addition, unveil manyparallels and connections between the EM algorithm and Markov chainMonte Carlo algorithms. Thorough discussions on the complexitiesand drawbacks that arise from the basic EM algorithm, such as slowconvergence and lack of an in-built procedure to compute thecovariance matrix of parameter estimates, are also presented. While the general philosophy of the First Edition has beenmaintained, this timely new edition has been updated, revised, andexpanded to include: * New chapters on Monte Carlo versions of the EM algorithm andgeneralizations of the EM algorithm * New results on convergence, including convergence of the EMalgorithm in constrained parameter spaces * Expanded discussion of standard error computation methods, suchas methods for categorical data and methods based on numericaldifferentiation * Coverage of the interval EM, which locates all stationary pointsin a designated region of the parameter space * Exploration of the EM algorithm's relationship with the Gibbssampler and other Markov chain Monte Carlo methods * Plentiful pedagogical elements--chapter introductions,lists of examples, author and subject indices, computer-drawngraphics, and a related Web site The EM Algorithm and Extensions, Second Edition serves as anexcellent text for graduate-level statistics students and is also acomprehensive resource for theoreticians, practitioners, andresearchers in the social and physical sciences who would like toextend their knowledge of the EM algorithm.

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  • Produktdetails
  • Verlag: John Wiley & Sons
  • Seitenzahl: 384
  • Erscheinungstermin: 02.04.2008
  • Englisch
  • ISBN-13: 9780470191606
  • Artikelnr.: 37291460
Autorenporträt
Geoffrey J. McLachlan, PhD, DSc, is Professor of Statistics in the Department of Mathematics at The University of Queensland, Australia. A Fellow of the American Statistical Association and the Australian Mathematical Society, he has published extensively on his research interests, which include cluster and discriminant analyses, image analysis, machine learning, neural networks, and pattern recognition. Dr. McLachlan is the author or coauthor of Analyzing Microarray Gene Expression Data, Finite Mixture Models, and Discriminant Analysis and Statistical Pattern Recognition, all published by Wiley. Thriyambakam Krishnan, PhD, is Chief Statistical Architect, SYSTAT Software at Cranes Software International Limited in Bangalore, India. Dr. Krishnan has over forty-five years of research, teaching, consulting, and software development experience at the Indian Statistical Institute (ISI). His research interests include biostatistics, image analysis, pattern recognition, psychometry, and the EM algorithm.
Inhaltsangabe
Preface to the Second Edition.

Preface to the First Edition.

List of Examples.

1. General Introduction.
2. Examples of the EM Algorithm.

3. Basic Theory of the EM Algorithm.

4. Standard Errors and Speeding up Convergence.

5. Extension of the EM Algorithm.

6. Monte Carlo Versions of the EM Algorithm.

7. Some Generalization of the EM Algorithm.

8. Further Applications of the EM Algorithm.

References.

Author Index.

Subject Index.
Rezensionen
"The EM Algorithm and Extension, Second Edition , serves as an excellent text for graduate-level statistics students and is also a comprehensive resource for theoreticians, practioners, and researchers in the social and physical sciences who would like to extend their knowledge of the EM algorithm." ( Mathematical Review, Issue 2009e)