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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.
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.
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