EM Algorithms from a Non-stochastic Perspective

Living reference work entry

Abstract

The EM algorithm is not a single algorithm, but a template for the construction of iterative algorithms. While it is always presented in stochastic language, relying on conditional expectations to obtain a method for estimating parameters in statistics, the essence of the EM algorithm is not stochastic. The conventional formulation of the EM algorithm given in many texts and papers on the subject is inadequate. A new formulation is given here based on the notion of acceptable data.

Keywords

Probability Density Function Expectation Maximization Expectation Maximization Algorithm Continuous Case Discrete Case 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

I wish to thank Professor Paul Eggermont of the University of Delaware for helpful discussions on these matters.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Mathematical Sciences University of Massachusetts LowellLowellUSA

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