EM Algorithms

Charles Byrne () and Paul P. B. Eggermont ()
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Charles Byrne: University of Massachusetts Lowell, Department of Mathematical Sciences
Paul P. B. Eggermont: University of Delaware, Food and Resource Economics

A chapter in Handbook of Mathematical Methods in Imaging, 2015, pp 305-388 from Springer

Abstract: Abstract Expectation-maximization algorithms, or em algorithms for short, are iterative algorithms designed to solve maximum likelihood estimation problems. The general setting is that one observes a random sample Y 1, Y 2, …, Y n of a random variable Y whose probability density function (pdf) f ( ⋅ | x o ) $$f(\,\cdot \,\vert \,x_{o})$$ with respect to some (known) dominating measure is known up to an unknown “parameter” x o . The goal is to estimate x o and, one might add, to do it well. In this chapter, that means to solve the maximum likelihood problem.

Date: 2015
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DOI: 10.1007/978-1-4939-0790-8_8

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