What is the expectation maximization algorithm?

@article{Do2008WhatIT,
  title={What is the expectation maximization algorithm?},
  author={Chuong B. Do and Serafim Batzoglou},
  journal={Nature Biotechnology},
  year={2008},
  volume={26},
  pages={897-899}
}
The expectation maximization algorithm arises in many computational biology applications that involve probabilistic models. What is it good for, and how does it work? 
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328 Citations
Highly Influential Citations
13
Background Citations
36
Methods Citations
124
Results Citations
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328 Citations

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Citation Type

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A thesis submitted in partial ful lment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.
Improving the Performance and Understanding of the Expectation Maximization Algorithm: Evolutionary and Visualization Methods
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This work proposes a genetic algorithm for expectation maximization (GAEM), where it is found that small population sizes are sufficient to produce high solution quality and considerable speed-up compared to the traditional EM algorithm and develops an age-layered EM algorithm, ALEM, which enables comparisons between similarly aged EM runs and discards less promising EM runs well before their convergence.
EM*: An EM Algorithm for Big Data
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The strategy is to embed EM-T into a non-linear hierarchical data structure (heap) that allows us to separate data that needs to be revisited from data that does not and narrow the iteration toward the data that is more difficult to cluster.
Belief Revision and the EM Algorithm
This paper provides a natural interpretation of the EM algorithm as a succession of revision steps that try to find a probability distribution in a parametric family of models in agreement with…
Model selection in biological networks using a graphical EM algorithm
Using data to build a better EM: EM* for big data
TLDR
The strategy is to embed EM-T into a nonlinear hierarchical data structure (heap) that allows us to separate data that needs to be revisited from data that does not and narrow the iteration toward the data that is more difficult to cluster.
An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data
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BEEM addresses a key bottleneck in “systems analysis” of microbiomes by enabling accurate inference of ecological models from high throughput sequencing data without the need for experimental biomass measurements.
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