The EM Algorithm and Related Statistical Models Statistics: A Series of Textbooks and Monographs Series
Coordonnateurs : Watanabe Michiko, Yamaguchi Kazunori
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.
Date de parution : 10-2019
15.2x22.9 cm
Date de parution : 10-2003
Ouvrage de 250 p.
15.2x22.9 cm
Thèmes de The EM Algorithm and Related Statistical Models :
Mots-clés :
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