The EM Algorithm and Related Statistical Models
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Chapters 5 and 6 discuss how to extend the CE method for various optimization problems. Chapter 5 introduces a fully automated version of the CE algorithm (FACE) where all the parameters of the CE algorithm are automatically tuned. Chapter 6 considers the problem of optimization for noisy objective functions. Chapter 7 discusses applications of the CE method to some combinatorial optimization problems such as pairwise sequence alignment problems in bioinformatics, scheduling problems in operation research, and clique problem in graph theory. Finally, in Chapter 8, applications of the CE method to some problems in machine learning are presented. Various Matlab implementations of CE algorithms are given in the Appendix. Since the CE method is a young and developing field, there is no book available in this area where the two authors are the pioneers. Therefore, it is quite a unique book and it may become a classic reference in the CE method literature.
[1] D. Helsel. Nondetects and data analysis : statistics for censored environmental data , 2005 .
[2] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[3] Gonzalo R. Arce,et al. Nonlinear Signal Processing - A Statistical Approach , 2004 .
[4] Thomas R. Gatliffe,et al. Nondetects and Data Analysis , 2006, Technometrics.
[5] P. Dixon. Nondetects and Data Analysis: Statistics for Censored Environmental Data , 2006 .