Statistical models called hidden Markov models are a recurring theme in computational biology. What are hidden Markov models, and why are they so useful for so many different problems?
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Rabiner, L.R. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77, 257–286 (1989).
Durbin, R., Eddy, S.R., Krogh, A. & Mitchison, G.J. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids (Cambridge University Press, Cambridge UK, 1998).
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Eddy, S. What is a hidden Markov model?. Nat Biotechnol 22, 1315–1316 (2004). https://doi.org/10.1038/nbt1004-1315
Issue Date:
DOI: https://doi.org/10.1038/nbt1004-1315
This article is cited by
-
Using eye-tracking for real-time translation: a new approach to improving reading experience
CCF Transactions on Pervasive Computing and Interaction (2024)
-
Speech recognition based on the transformer's multi-head attention in Arabic
International Journal of Speech Technology (2024)
-
Acute and chronic stress alter behavioral laterality in dogs
Scientific Reports (2023)
-
The flip-flop neuron: a memory efficient alternative for solving challenging sequence processing and decision-making problems
Neural Computing and Applications (2023)
-
Modeling Markov sources and hidden Markov models by P systems
Journal of Membrane Computing (2023)