What is recorded here?
Presented a general expectation–maximization framework for iteratively computing maximum-likelihood estimates from incomplete-data problems and established monotonic likelihood behavior under its stated conditions.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The record is the exact 1977 JRSS Series B paper. Earlier special-case missing-data procedures, the Baum–Welch algorithm, later convergence refinements, variational methods and software implementations remain separate.
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People and systems
A. P. Dempster; N. M. Laird; D. B. Rubin
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.
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Documented countries / regions: United States
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English catalogue narrative
Dempster, Laird and Rubin: maximum likelihood from incomplete data via the EM algorithm
Presented a general expectation–maximization framework for iteratively computing maximum-likelihood estimates from incomplete-data problems and established monotonic likelihood behavior under its stated conditions.
Scientific scope
The record is the exact 1977 JRSS Series B paper. Earlier special-case missing-data procedures, the Baum–Welch algorithm, later convergence refinements, variational methods and software implementations remain separate.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
A. P. Dempster; N. M. Laird; D. B. Rubin
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.