The EM Algorithm and Extensions (eBook)

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 developments from the past
decade, The EM Algorithm and Extensions, Second Edition
successfully 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 discuss convergence issues and
computation of standard errors, and, in addition, unveil many
parallels and connections between the EM algorithm and Markov chain
Monte Carlo algorithms. Thorough discussions on the complexities
and drawbacks that arise from the basic EM algorithm, such as slow
convergence and lack of an in-built procedure 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 edition has been updated, revised, and
expanded to include:

* New chapters on 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 for categorical 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 algorithm'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.