Construct · 1 fields · 1 citations

expectation-maximization algorithm: a standalone construct

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Statistics

an iterative method for maximum likelihood with missing or latent data, alternating expected completion and maximization steps.

In statistics, expectation-maximization algorithm refers to an iterative method for maximum likelihood with missing or latent data, alternating expected completion and maximization steps. This meaning comes from Dempster (1977) in Journal of the Royal Statistical Society Series B: Statistical Methodology.[1]

Unit
Recovery means
Measured by

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Nearby constructs

Dawid-Skene model1 fields · 1 citationsanchor · a standalone construct latent4 fields · 6 citationsjingle · one word, four constructs likelihood3 fields · 3 citationsjingle · one word, three constructs

References

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: Statistical Methodology, 39(1), 1-22. https://doi.org/10.1111/j.2517-6161.1977.tb01600.x

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