Dempster-Laird-Rubin EM algorithm

A general iterative method for maximum-likelihood estimation from incomplete data, published in 1977 by Arthur Dempster, Nan Laird, and Donald Rubin ("Maximum likelihood from incomplete data via the EM algorithm," Journal of the Royal Statistical Society, Series B, 39, 1-38) — one of the most widely cited papers in statistics, later applied across latent-variable modeling, clustering, and machine learning generally. Matters to this vault as the numerical method Dawid & Skene (1979) explicitly credits and depends on to fit their own observer-error-rate model: the previously-unread ancestor standing one citation hop behind a lineage the vault had already traced from a 1979 clinical-medicine paper through 2014 crowdsourcing research to 2025 LLM retrieval.

References

• 2026-08-14: Dawid & Skene (1979) name this paper directly as supplying "a numerical method of maximum likelihood estimation which is ideally suited to" their own problem — extending the vault's Dawid-Skene citation lineage one hop further back. (claim-dawid-skene-1979-credits-dempster-laird-rubin-1977-for-em-method)