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What is the expectation maximization algorithm?

Nature Biotechnology volume 26, pages 897–899 (2008)Cite this article

The expectation maximization algorithm arises in many computational biology applications that involve probabilistic models. What is it good for, and how does it work?

Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust procedures for learning parameters from observations. Often, however, the only data available for training a probabilistic model are incomplete. Missing values can occur, for example, in medical diagnosis, where patient histories generally include results from a limited battery of tests. Alternatively, in gene expression clustering, incomplete data arise from the intentional omission of gene-to-cluster assignments in the probabilistic model. The expectation maximization algorithm enables parameter estimation in probabilistic models with incomplete data.

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Figure 1: Parameter estimation for complete and incomplete data.

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Acknowledgements

C.B.D. is supported in part by an National Science Foundation (NSF) Graduate Fellowship. S.B. wishes to acknowledge support by the NSF CAREER Award. We thank four anonymous referees for helpful suggestions.

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  1. Chuong B. Do and Serafim Batzoglou are in the Computer Science Department, Stanford University, 318 Campus Drive, Stanford, California 94305-5428, USA.,

    Chuong B Do & Serafim Batzoglou

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  1. Chuong B Do
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  2. Serafim Batzoglou
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Correspondence to Chuong B Do.

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Do, C., Batzoglou, S. What is the expectation maximization algorithm?. Nat Biotechnol 26, 897–899 (2008). https://doi.org/10.1038/nbt1406

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