The EM Algorithm in Genetics, Genomics and Public Health

2010/11/01 by Nan M. Laird · 1 voice
Biochemistry, Genetics and Molecular Biology · Mathematics · #Fractal and DNA sequence analysis #Gene expression and cancer classification #Machine Learning in Bioinformatics #q-bio.QM #stat.ME

paper · pdf · doi:10.1214/08-sts270

published as Statistical Science 2010, Vol. 25, No. 4, 450-457 · Published in at http://dx.doi.org/10.1214/08-STS270 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

openalex publication_date 2010/11/01 · arxiv created 2011/04/11 · arxiv published 2011/04/11 · arxiv updated 2011/04/12 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

The popularity of the EM algorithm owes much to the 1977 paper by Dempster, Laird and Rubin. That paper gave the algorithm its name, identified the general form and some key properties of the algorithm and established its broad applicability in scientific research. This review gives a nontechnical introduction to the algorithm for a general scientific audience, and presents a few examples characteristic of its application.

Citations

• A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains

Discussions

• Remains Very important in genomics/sequence analysis applications where you can t brute force given the scale. And the algorithms usually take the randomized forms in the field arxiv.org/pdf/1104.192 [bsky, 3 points, 0 comments]

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