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.
Author(s): Michiko Watanabe, Kazunori Yamaguchi
Publisher: CRC Press
Year: 2003
Language: English
Pages: 250
Tags: Математика;Теория вероятностей и математическая статистика;Математическая статистика;
Preface......Page 10
Contents......Page 12
Contributors......Page 16
Incomplete Data and the Generation Mechanisms......Page 18
Incomplete Data and the EM Algorithm......Page 26
Statistical Models and the EM Algorithm......Page 38
Robust Model and the EM Algorithm......Page 54
Latent Structure Model and the EM Algorithm......Page 82
Extensions of the EM Algorithm......Page 88
Convergence Speed and Acceleration of the EM Algorithm......Page 102
EM Algorithm in Neural Network Learning......Page 112
Markov Chain Monte Carlo......Page 144
Appendix A: SOLASTM 3.0 for Missing Data Analysis......Page 176
Appendix B: l EM......Page 206
Index......Page 212
