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Hidden Markov Models

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Encyclopedia of Machine Learning and Data Mining

Abstract

Starting from the concept of regular Markov models we introduce the concept of hidden Markov model, and the issue of estimating the output emission and transition probabilities between hidden states, for which the Baum-Welch algorithm is the standard choice. We mention typical application in which hidden Markov models play a central role, and mention a number of popular implementations.

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Author information

Authors and Affiliations

  1. Centre for Language Studies, Radboud University, Nijmegen, The Netherlands

    Antal van den Bosch

Authors
  1. Antal van den Bosch
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Correspondence to Antal van den Bosch .

Editor information

Editors and Affiliations

  1. The University of New South Wales, Sydney, NSW, Australia

    Claude Sammut

  2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

    Geoffrey I. Webb

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van den Bosch, A. (2017). Hidden Markov Models. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_124

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