Hidden Markov model (HMM) is a powerful mathematical tool for prediction and recognition. Many computer software products implement HMM and hide its complexity, which assist scientists to use HMM for applied researches. However comprehending HMM in order to take advantages of its strong points requires a lot of efforts. This report is a tutorial on HMM with full of mathematical proofs and example, which help researchers to understand it by the fastest way from theory to practice. The report focuses on three common problems of HMM such as evaluation problem, uncovering problem, and learning problem, in which learning problem with support of optimization theory is the main subject.
| Published in |
Applied and Computational Mathematics (Volume 6, Issue 4-1)
This article belongs to the Special Issue Some Novel Algorithms for Global Optimization and Relevant Subjects |
| DOI | 10.11648/j.acm.s.2017060401.12 |
| Page(s) | 16-38 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2016. Published by Science Publishing Group |
Hidden Markov Model, Optimization, Evaluation Problem, Uncovering Problem, Learning Problem
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APA Style
Loc Nguyen. (2016). Tutorial on Hidden Markov Model. Applied and Computational Mathematics, 6(4-1), 16-38. https://doi.org/10.11648/j.acm.s.2017060401.12
ACS Style
Loc Nguyen. Tutorial on Hidden Markov Model. Appl. Comput. Math. 2016, 6(4-1), 16-38. doi: 10.11648/j.acm.s.2017060401.12
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Download@article{10.11648/j.acm.s.2017060401.12,
author = {Loc Nguyen},
title = {Tutorial on Hidden Markov Model},
journal = {Applied and Computational Mathematics},
volume = {6},
number = {4-1},
pages = {16-38},
doi = {10.11648/j.acm.s.2017060401.12},
url = {https://doi.org/10.11648/j.acm.s.2017060401.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.s.2017060401.12},
abstract = {Hidden Markov model (HMM) is a powerful mathematical tool for prediction and recognition. Many computer software products implement HMM and hide its complexity, which assist scientists to use HMM for applied researches. However comprehending HMM in order to take advantages of its strong points requires a lot of efforts. This report is a tutorial on HMM with full of mathematical proofs and example, which help researchers to understand it by the fastest way from theory to practice. The report focuses on three common problems of HMM such as evaluation problem, uncovering problem, and learning problem, in which learning problem with support of optimization theory is the main subject.},
year = {2016}
}
TY - JOUR T1 - Tutorial on Hidden Markov Model AU - Loc Nguyen Y1 - 2016/06/17 PY - 2016 N1 - https://doi.org/10.11648/j.acm.s.2017060401.12 DO - 10.11648/j.acm.s.2017060401.12 T2 - Applied and Computational Mathematics JF - Applied and Computational Mathematics JO - Applied and Computational Mathematics SP - 16 EP - 38 PB - Science Publishing Group SN - 2328-5613 UR - https://doi.org/10.11648/j.acm.s.2017060401.12 AB - Hidden Markov model (HMM) is a powerful mathematical tool for prediction and recognition. Many computer software products implement HMM and hide its complexity, which assist scientists to use HMM for applied researches. However comprehending HMM in order to take advantages of its strong points requires a lot of efforts. This report is a tutorial on HMM with full of mathematical proofs and example, which help researchers to understand it by the fastest way from theory to practice. The report focuses on three common problems of HMM such as evaluation problem, uncovering problem, and learning problem, in which learning problem with support of optimization theory is the main subject. VL - 6 IS - 4-1 ER -
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