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... > entity > continuant > generically dependent continuant > information content entity > directive information entity > plan specification > algorithm > clustering algorithm > expectation–maximization algorithm

Preferred term

expectation–maximization algorithm  

Definition

  • expectation–maximization 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. (Wikipedia, retrieved on 2021/07/02).

Broader concept

Entry terms

  • EM algorithm

Bibliographic citation(s)

  • • Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39(1), 1‑38.

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URI

http://data.loterre.fr/ark:/67375/LTK-B0PJD5PB-4

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