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This text provides a comprehensive description of a method of constructing a statistical model when only incomplete data is available. It then proposes specific estimation algorithms for solving various individual incomplete data problems. It discusses the realistic problems on the processing of missing values commonly seen in multidimensional data and the various methods to cope with them.

Exploring the application and formulation of the EM algorithm, The EM Algorithm and Related Statistical Models offers a valuable method for constructing statistical models when only incomplete information is available, and proposes specific estimation algorithms for solutions to incomplete data problems. The text covers current topics including statistical models with latent variables, as well as neural network models, and Markov Chain Monte Carlo methods. It describes software resources valuable for the processing of the EM algorithm with incomplete data and for general analysis of latent...