The knee-jerk mapping
Peter G. Doyle, Jim Reeds
TL;DR
This paper provides a comprehensive theoretical framework for the 'knee-jerk mapping', foundational to the EM algorithm used in optimization tasks.
Contribution
It offers the definitive theory of the knee-jerk mapping, clarifying its role in the EM algorithm's development and application.
Findings
Formalizes the theory of the knee-jerk mapping
Connects the mapping to the EM algorithm's principles
Clarifies the mathematical basis of the optimization process
Abstract
We claim to give the definitive theory of what we call the `knee-jerk mapping', which is the basis for a class of optimization algorithms introduced by Baum, and promoted by Dempster, Laird, and Rubin under the name `EM algorithm'.
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Taxonomy
TopicsMachine Learning and Algorithms · Bayesian Methods and Mixture Models · Advanced Statistical Methods and Models