The EM Algorithm and Extensions, Second Edition

McLachlan, and Krishnan (2008). Wiley. 359 pages.

The only single-source - now completely updated and revised - to offer a unified treatment of the theory, methodology and applications of the EM algorithm

Complete with updates that capture the developments from the past decade The EM Algorithm and Extensions, Second Edition sucessfully provides a basic understanding of the EM algorithm by describing its inception, implementation, and applicability in numerous statistical contexts. In conjunction with the fundamentals of the topic, the authors describe convergence issues and computation of standard errors, and in aaddition, unveil many parallels between the EM algorith amd Markov Chain Monte Carlo algorithms. Through discussionson the complexities and drawbacks that arise from the basic EM algorithm, such as slow convergence and the lack of an in- builtprocedure to compute the covariance matrix of parameter estimates, are also presented.

While the general philosophy of the First Edition has been maintained, this timely new editionhas been, updated, revised, and expanded to include:

• New chapterson the Monte Carlo versions of the EM algorithm and generalizations of the EM algorithm
• New results on convergence, including convergence of the EM algorithm in constrained parameter spaces
• Expanded discussion of standard error computation methods, such as methods forcategorical data and methods based on numerical differentiation
• Coverage of the interval EM which locates all stationary points in a designated region of the parameter space
• Exploration of the Em alogorithm's relationship with the Gibbs sampler and other Markov chain Monte Carlo methods
• Plentiful pedagogical elements - chapter introductions, lists of examples, author and subject indices, computer drawn graphics and a related web site

The EM Algorithm and Extensions, Second Edition serves as an excellent text for graduate-level statistics students and is also a comprehensive resource for theoreticians, practitioners, and researchers in the social and physical sciences who would like to extend their knowledge of the EM algorithm.