The expectation-maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori estimates of parameters in statistical models whose likelihood depends on unobserved latent variables. It alternates between an expectation step, which computes the expected value of the log-likelihood using the current parameter estimate, and a maximization step, which computes new parameters maximizing that expectation. Arthur Dempster, Nan Laird and Donald Rubin formalized and named the algorithm in a widely cited 1977 paper, though earlier researchers had proposed versions of the method in specific contexts before that. This description is adapted from Wikipedia contributors under CC BY-SA 4.0; changes were made. https://creativecommons.org/licenses/by-sa/4.0/
Facts
Credited ToArthur Dempster, Nan Laird and Donald Rubin, 1977 (formalized and named); earlier context-specific versions by others including Cedric Smith and H. O. Hartley. 1 Connections
In Field
Source Wikipedia: Expectation Maximization Algorithm
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1. Wikipedia: Expectation Maximization Algorithm
Wikimedia FoundationLead section
In statistics, an expectation-maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables.
History
The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin.
- In Field: Machine Learning, Lead paragraph
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