Description
The EM Algorithm and Related Statistical Models
Statistics: A Series of Textbooks and Monographs Series
Coordinators: Watanabe Michiko, Yamaguchi Kazunori
Language: EnglishSubjects for The EM Algorithm and Related Statistical Models:
Keywords
Em Algorithm; Multivariate Normal Distribution Model; Conditional Expectation; Missing Data; MCEM Algorithm; Missing Values; Newton Raphson Method; Missingness Pattern; DA Algorithm; GS Algorithm; Latent Structure Model; MCEM; Boltzmann Machine; MCMC Method; Model Manifold; Original Em; Nonnegative Definite; Posterior Distribution; Imputed Data; REML Estimation; Asymptotic Variance Covariance Matrix; Mixed Linear Model; quasi-Newton Method; Hidden Units; Parameter Update Rule
Publication date: 10-2019
· 15.2x22.9 cm · Paperback
Publication date: 10-2003
250 p. · 15.2x22.9 cm · Hardback
Description
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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 structure models of categorical data, and studies accelerated versions of the EM algorithm.