Editing Expectation–maximization algorithm (section)

== External links ==
* Various 1D, 2D and 3D [http://wiki.stat.ucla.edu/socr/index.php/SOCR_EduMaterials_Activities_2D_PointSegmentation_EM_Mixture demonstrations of EM together with Mixture Modeling] are provided as part of the paired [[SOCR]] activities and applets. These applets and activities show empirically the properties of the EM algorithm for parameter estimation in diverse settings.
* [https://github.com/l-/CommonDataAnalysis Class hierarchy] in [[C++]] (GPL) including Gaussian Mixtures
* [http://www.inference.phy.cam.ac.uk/mackay/itila/ The on-line textbook: Information Theory, Inference, and Learning Algorithms], by [[David J.C. MacKay]] includes simple examples of the EM algorithm such as clustering using the soft ''k''-means algorithm, and emphasizes the variational view of the EM algorithm, as described in Chapter 33.7 of version 7.2 (fourth edition).
* [http://www.cse.buffalo.edu/faculty/mbeal/papers/beal03.pdf Variational Algorithms for Approximate Bayesian Inference], by M. J. Beal includes comparisons of EM to Variational Bayesian EM and derivations of several models including Variational Bayesian HMMs ([http://www.cse.buffalo.edu/faculty/mbeal/thesis/index.html chapters]).
* [http://www.seanborman.com/publications/EM_algorithm.pdf The Expectation Maximization Algorithm: A short tutorial], A self-contained derivation of the EM Algorithm by Sean Borman.
* [http://pages.cs.wisc.edu/~jerryzhu/cs838/EM.pdf The EM Algorithm], by Xiaojin Zhu.
* [https://arxiv.org/abs/1105.1476 EM algorithm and variants: an informal tutorial] by Alexis Roche. A concise and very clear description of EM and many interesting variants.

{{DEFAULTSORT:Expectation-maximization Algorithm}}
[[Category:Estimation methods]]
[[Category:Machine learning algorithms]]
[[Category:Missing data]]
[[Category:Statistical algorithms]]
[[Category:Optimization algorithms and methods]]
[[Category:Cluster analysis algorithms]]