A Tutorial on the Expectation-Maximization Algorithm Including Maximum-Likelihood Estimation and EM Training of Probabilistic Context-Free Grammars

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dc.creatorPrescher, Detlef
dc.date2004-12-03
dc.date2005-03-11
dc.date.accessioned2026-07-07T03:22:08Z
dc.date.available2026-07-07T03:22:08Z
dc.descriptionThe paper gives a brief review of the expectation-maximization algorithm (Dempster 1977) in the comprehensible framework of discrete mathematics. In Section 2, two prominent estimation methods, the relative-frequency estimation and the maximum-likelihood estimation are presented. Section 3 is dedicated to the expectation-maximization algorithm and a simpler variant, the generalized expectation-maximization algorithm. In Section 4, two loaded dice are rolled. A more interesting example is presented in Section 5: The estimation of probabilistic context-free grammars.
dc.descriptionPresented at the 15th European Summer School in Logic, Language and Information (ESSLLI 2003). Example 5 extended (and partially corrected)
dc.identifierhttps://arxiv.org/abs/cs/0412015
dc.identifierhttp://arxiv.org/abs/cs/0412015
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/32475
dc.subjectComputation and Language
dc.titleA Tutorial on the Expectation-Maximization Algorithm Including Maximum-Likelihood Estimation and EM Training of Probabilistic Context-Free Grammars
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