papers about EM algorithm

• Arthur Dempster, Nan Laird, and Donald Rubin. “Maximum likelihood from incomplete data via the EM algorithm”. Journal of the Royal Statistical Society, Series B, 39(1):1–38, 1977 [1].
• Robert Hogg, Joseph McKean and Allen Craig. Introduction to Mathematical Statistics. pp. 359-364. Upper Saddle River, NJ: Pearson Prentice Hall, 2005.
• Radford Neal, Geoffrey Hinton. “A view of the EM algorithm that justifies incremental, sparse, and other variants”. In Michael I. Jordan (editor), Learning in Graphical Models pp 355-368. Cambridge, MA: MIT Press, 1999.
• The on-line textbook: Information Theory, Inference, and Learning Algorithms, by David J.C. MacKay includes simple examples of the E-M algorithm such as clustering using the soft K-means algorithm, and emphasizes the variational view of the E-M algorithm.
• A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models, by J. Bilmes includes a simplified derivation of the EM equations for Gaussian Mixtures and Gaussian Mixture Hidden Markov Models.
• Information Geometry of the EM and em Algorithms for Neural Networks, by Shun-Ichi Amari give a view of EM algorithm from geometry view point.