McLachlan G.J., Krishnan T. The EM Algorithm and Extensions

2nd Edition. — Wiley, 2008. — 400 p. — (Wiley Series in Probability and Statistics). — ISBN: 978-0-471-20170-0.The only single-source — —now completely updated and revised — —to offer a unified treatment of the theory, methodology, and applications of the EM algorithm
omplete 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.List of Examples
General Introduction
Examples of the Em Algorithm
Basic Theory of the Em Algorithm
Standard Errors and Speeding Up Convergence
Extensions of the Em Algorithm
Monte Carlo Versions of the Em Algorithm
Some Generalizations of the Em Algorithm
Further Applications of the Em Algorithm