Relevant bibliographies by topics / Hiden Markov model

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  2. Dissertations / Theses
  3. Books
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Academic literature on the topic 'Hiden Markov model'

Author: Grafiati
Published: 1 July 2022
Last updated: 31 July 2025

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Journal articles on the topic "Hiden Markov model"

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Buckby, Jodie, Ting Wang, Jiancang Zhuang, and Kazushige Obara. "Model Checking for Hidden Markov Models." Journal of Computational and Graphical Statistics 29, no. 4 (2020): 859–74. http://dx.doi.org/10.1080/10618600.2020.1743295.

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Grewal, Jasleen K., Martin Krzywinski, and Naomi Altman. "Markov models — hidden Markov models." Nature Methods 16, no. 9 (2019): 795–96. http://dx.doi.org/10.1038/s41592-019-0532-6.

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Tumilaar, Kezia, Yohanes Langi, and Altien Rindengan. "Hidden Markov Model." d'CARTESIAN 4, no. 1 (2015): 86. http://dx.doi.org/10.35799/dc.4.1.2015.8104.

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Hidden Markov Models (HMM) is a stochastic model and is essentially an extension of Markov Chain. In Hidden Markov Model (HMM) there are two types states: the observable states and the hidden states. The purpose of this research are to understand how hidden Markov model (HMM) and to understand how the solution of three basic problems on Hidden Markov Model (HMM) which consist of evaluation problem, decoding problem and learning problem. The result of the research is hidden Markov model can be defined as . The evaluation problem or to compute probability of the observation sequence given the mo
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Kim, Sae-Joong, Young-Han Jung, and Chong-Kwan Heo. "Analysis sports using the Hidden Markov Model." Korean Journal of Sports Science 26, no. 3 (2017): 1301–9. http://dx.doi.org/10.35159/kjss.2017.06.26.3.1301.

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Lay, Khin Khin, and Aung Cho. "Myanmar Named Entity Recognition with Hidden Markov Model." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (2019): 1144–47. http://dx.doi.org/10.31142/ijtsrd24012.

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Johansson, Mathias, and Tomas Olofsson. "Bayesian Model Selection for Markov, Hidden Markov, and Multinomial Models." IEEE Signal Processing Letters 14, no. 2 (2007): 129–32. http://dx.doi.org/10.1109/lsp.2006.882094.

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Bhatia, Varsha. "Applications of Hidden Markov Model in Wireless Sensor Network." International Journal of Psychosocial Rehabilitation 24, no. 4 (2020): 6549–57. http://dx.doi.org/10.37200/ijpr/v24i4/pr2020465.

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Ghods, Vahid, and Mohammad Karim Sohrabi. "Online Farsi Handwritten Character Recognition Using Hidden Markov Model." Journal of Computers 11, no. 2 (2016): 169–75. http://dx.doi.org/10.17706/jcp.11.2.169-175.

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Ye, Fei, and Yifei Wang. "A Novel Method for Decoding Any High-Order Hidden Markov Model." Discrete Dynamics in Nature and Society 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/231704.

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This paper proposes a novel method for decoding any high-order hidden Markov model. First, the high-order hidden Markov model is transformed into an equivalent first-order hidden Markov model by Hadar’s transformation. Next, the optimal state sequence of the equivalent first-order hidden Markov model is recognized by the existing Viterbi algorithm of the first-order hidden Markov model. Finally, the optimal state sequence of the high-order hidden Markov model is inferred from the optimal state sequence of the equivalent first-order hidden Markov model. This method provides a unified algorithm
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Eddy, Sean R. "Hidden Markov models." Current Opinion in Structural Biology 6, no. 3 (1996): 361–65. http://dx.doi.org/10.1016/s0959-440x(96)80056-x.

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Dissertations / Theses on the topic "Hiden Markov model"

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Devilliers, Esther. "Modélisation micro-économétrique des choix de pratiques de production et des utilisations d'intrants chimiques des agriculteurs : une approche par les fonctions de production latentes." Thesis, Rennes, Agrocampus Ouest, 2021. http://www.theses.fr/2021NSARE058.

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La notion d’itinéraire technique est une notion agronomique qui nous permet d’appréhender l’imbrication entre les rendements objectifs et les niveaux d’utilisation d’intrants associés. Dès lors, on peut admettre qu’à différents types d’itinéraires techniques correspondent différentes fonctions de production. Modéliser ces différentes fonctions est une des clés pour mieux comprendre la dépendance de certaines pratiques culturales aux pesticides et de ce fait constitue un enjeu majeur pour concevoir les futures politiques publiques.Intégrer cette notion d’itinéraire technique nécessite de tenir
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Kotsalis, Georgios. "Model reduction for Hidden Markov models." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38255.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.<br>Includes bibliographical references (leaves 57-60).<br>The contribution of this thesis is the development of tractable computational methods for reducing the complexity of two classes of dynamical systems, finite alphabet Hidden Markov Models and Jump Linear Systems with finite parameter space. The reduction algorithms employ convex optimization and numerical linear algebra tools and do not pose any structural requirements on the systems at hand. In the Jump Linear Systems case, a distance metric
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Kadhem, Safaa K. "Model fit diagnostics for hidden Markov models." Thesis, University of Plymouth, 2017. http://hdl.handle.net/10026.1/9966.

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Hidden Markov models (HMMs) are an efficient tool to describe and model the underlying behaviour of many phenomena. HMMs assume that the observed data are generated independently from a parametric distribution, conditional on an unobserved process that satisfies the Markov property. The model selection or determining the number of hidden states for these models is an important issue which represents the main interest of this thesis. Applying likelihood-based criteria for HMMs is a challenging task as the likelihood function of these models is not available in a closed form. Using the data augm
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Bulla, Jan. "Application of Hidden Markov and Hidden Semi-Markov Models to Financial Time Series." Doctoral thesis, [S.l. : s.n.], 2006. http://swbplus.bsz-bw.de/bsz260867136inh.pdf.

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Wynne-Jones, Michael. "Model building in neural networks with hidden Markov models." Thesis, University of Edinburgh, 1994. http://hdl.handle.net/1842/284.

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This thesis concerns the automatic generation of architectures for neural networks and other pattern recognition models comprising many elements of the same type. The requirement for such models, with automatically determined topology and connectivity, arises from two needs. The first is the need to develop commercial applications of the technology without resorting to laborious trial and error with different network sizes; the second is the need, in large and complex pattern processing applications such as speech recognition, to optimise the allocation of computing resources for problem solvi
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Tillman, Måns. "On-Line Market Microstructure Prediction Using Hidden Markov Models." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208312.

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Over the last decades, financial markets have undergone dramatic changes. With the advent of the arbitrage pricing theory, along with new technology, markets have become more efficient. In particular, the new high-frequency markets, with algorithmic trading operating on micro-second level, make it possible to translate ”information” into price almost instantaneously. Such phenomena are studied in the field of market microstructure theory, which aims to explain and predict them. In this thesis, we model the dynamics of high frequency markets using non-linear hidden Markov models (HMMs). Such mo
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Hofer, Gregor Otto. "Speech-driven animation using multi-modal hidden Markov models." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/3786.

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The main objective of this thesis was the synthesis of speech synchronised motion, in particular head motion. The hypothesis that head motion can be estimated from the speech signal was confirmed. In order to achieve satisfactory results, a motion capture data base was recorded, a definition of head motion in terms of articulation was discovered, a continuous stream mapping procedure was developed, and finally the synthesis was evaluated. Based on previous research into non-verbal behaviour basic types of head motion were invented that could function as modelling units. The stream mapping meth
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McKee, Bill Frederick. "Optimal hidden Markov models." Thesis, University of Plymouth, 1999. http://hdl.handle.net/10026.1/1698.

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In contrast with training algorithms such as Baum-Welch, which produce solutions that are a local optimum of the objective function, this thesis describes the attempt to develop a training algorithm which delivers the global optimum Discrete ICdden Markov Model for a given training sequence. A total of four different methods of attack upon the problem are presented. First, after building the necessary analytical tools, the thesis presents a direct, calculus-based assault featuring Matrix Derivatives. Next, the dual analytic approach known as Geometric Programming is examined and then adapted t
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Schimert, James. "A high order hidden Markov model /." Thesis, Connect to this title online; UW restricted, 1992. http://hdl.handle.net/1773/8939.

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Chong, Fong Ho. "Frequency-stream-tying hidden Markov model /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202003%20CHONG.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 119-123). Also available in electronic version. Access restricted to campus users.
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Books on the topic "Hiden Markov model"

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Westhead, David R., and M. S. Vijayabaskar, eds. Hidden Markov Models. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6753-7.

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Bouguila, Nizar, Wentao Fan, and Manar Amayri, eds. Hidden Markov Models and Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99142-5.

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Koski, Timo. Hidden Markov Models for Bioinformatics. Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0612-5.

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Mamon, Rogemar S., and Robert J. Elliott, eds. Hidden Markov Models in Finance. Springer US, 2007. http://dx.doi.org/10.1007/0-387-71163-5.

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Mamon, Rogemar S., and Robert J. Elliott, eds. Hidden Markov Models in Finance. Springer US, 2014. http://dx.doi.org/10.1007/978-1-4899-7442-6.

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Cappé, Olivier, Eric Moulines, and Tobias Rydén. Inference in Hidden Markov Models. Springer New York, 2005. http://dx.doi.org/10.1007/0-387-28982-8.

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Eric, Moulines, and Rydén Tobias 1966-, eds. Inference in hidden Markov models. Springer, 2005.

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Cappe, Olivier. Inference in hidden Markov models. Springer, 2005.

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Elliott, Robert J., and Rogemar S. Mamon. Hidden Markov models in finance. Springer, 2011.

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Zucchini, Walter, Iain L. MacDonald, and Roland Langrock. Hidden Markov Models for Time Series. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/b20790.

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Book chapters on the topic "Hiden Markov model"

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Awad, Mariette, and Rahul Khanna. "Hidden Markov Model." In Efficient Learning Machines. Apress, 2015. http://dx.doi.org/10.1007/978-1-4302-5990-9_5.

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Li, Hang. "Hidden Markov Model." In Machine Learning Methods. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3917-6_10.

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Blanco-Castañeda, Liliana, and Viswanathan Arunachalam. "Hidden Markov Model." In Applied Stochastic Modeling. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31282-3_5.

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Wu, Jun. "Hidden Markov model." In The Beauty of Mathematics in Computer Science. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315169491-5.

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Privault, Nicolas. "Hidden Markov Model." In Springer Undergraduate Mathematics Series. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65820-4_9.

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Sucar, Luis Enrique. "Hidden Markov Models." In Probabilistic Graphical Models. Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6699-3_5.

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Sucar, Luis Enrique. "Hidden Markov Models." In Probabilistic Graphical Models. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61943-5_5.

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Coelho, João Paulo, Tatiana M. Pinho, and José Boaventura-Cunha. "Autoregressive Markov Models." In Hidden Markov Models. CRC Press, 2019. http://dx.doi.org/10.1201/9780429261046-5.

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Fink, Gernot A. "Hidden Markov Models." In Markov Models for Pattern Recognition. Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6308-4_5.

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Coelho, João Paulo, Tatiana M. Pinho, and José Boaventura-Cunha. "Discrete Hidden Markov Models." In Hidden Markov Models. CRC Press, 2019. http://dx.doi.org/10.1201/9780429261046-3.

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Conference papers on the topic "Hiden Markov model"

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Citron, Levli, Kobi Cohen, and Qing Zhao. "Anomaly Search of a Hidden Markov Model." In 2024 IEEE International Symposium on Information Theory (ISIT). IEEE, 2024. http://dx.doi.org/10.1109/isit57864.2024.10619616.

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Farshchian, Maryam, and Majid Vafaei Jahan. "Stock market prediction with Hidden Markov Model." In 2015 International Congress on Technology, Communication and Knowledge (ICTCK). IEEE, 2015. http://dx.doi.org/10.1109/ictck.2015.7582714.

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Somani, Poonam, Shreyas Talele, and Suraj Sawant. "Stock market prediction using Hidden Markov Model." In 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). IEEE, 2014. http://dx.doi.org/10.1109/itaic.2014.7065011.

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Gupta, Aditya, and Bhuwan Dhingra. "Stock market prediction using Hidden Markov Models." In 2012 Students Conference on Engineering and Systems (SCES). IEEE, 2012. http://dx.doi.org/10.1109/sces.2012.6199099.

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Gupta, Kriti. "Hidden Markov Model." In the International Conference & Workshop. ACM Press, 2011. http://dx.doi.org/10.1145/1980022.1980326.

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Dimoulkas, Ilias, Mikael Amelin, and Mohammad Reza Hesamzadeh. "Forecasting balancing market prices using Hidden Markov Models." In 2016 13th International Conference on the European Energy Market (EEM). IEEE, 2016. http://dx.doi.org/10.1109/eem.2016.7521229.

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Ben Ayed, Alaidine, and S. Selouani. "Market customers classification using Hidden Markov Models toolkit." In 2013 International Conference on Computer Applications Technology (ICCAT 2013). IEEE, 2013. http://dx.doi.org/10.1109/iccat.2013.6521974.

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Saxena, Rishabh, Adarsh Upadhayay, Gaurav Raj, et al. "Analysis of Enhanced Hidden Markov Models for Improved Stock Market Price Forecasting and Prediction." In AICCONF '24: Cognitive Models and Artificial Intelligence Conference. ACM, 2024. http://dx.doi.org/10.1145/3660853.3660922.

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Kotsalis, Georgios, Alexandre Megretski, and Munther A. Dahleh. "A Model Reduction Algorithm for Hidden Markov Models." In Proceedings of the 45th IEEE Conference on Decision and Control. IEEE, 2006. http://dx.doi.org/10.1109/cdc.2006.377011.

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Hassan, M. R., and B. Nath. "Stock market forecasting using hidden Markov model: a new approach." In 5th International Conference on Intelligent Systems Design and Applications (ISDA'05). IEEE, 2005. http://dx.doi.org/10.1109/isda.2005.85.

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Reports on the topic "Hiden Markov model"

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Ghahramani, Zoubin, and Michael I. Jordan. Factorial Hidden Markov Models. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada307097.

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Ainsleigh, Phillip L. Theory of Continuous-State Hidden Markov Models and Hidden Gauss-Markov Models. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada415930.

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Yang, Jie, and Yangsheng Xu. Hidden Markov Model for Gesture Recognition. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada282845.

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Thrun, Sebastian, and John Langford. Monte Carlo Hidden Markov Models. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada363714.

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Hollis, Andrew, George Tompkins, Alyson Wilson, and Ralph Smith. Proliferation Monitoring with Hidden Markov Models. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1766975.

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Ricca, Bernard. Nonlinear Time Series Analyses (Part II). Instats Inc., 2024. https://doi.org/10.61700/0emwhpkidie951390.

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This four-day seminar delves into phenomenological modeling of time series, focusing on nonlinear dynamic methodologies such as sparse identification of nonlinear dynamics, dynamic mode decomposition, and hidden Markov models, all implemented using the R programming language. Participants will enhance their research skills by learning to dynamically model, analyze, and interpret time-dependent data, ultimately enabling them to conduct innovative and robust research across various fields.
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Del Rose, Michael S., Philip Frederick, and Christian Wagner. Using Evidence Feed-Forward Hidden Markov Models. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada543331.

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Balasubramanian, Vijay. Equivalence and Reduction of Hidden Markov Models. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada270762.

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Chan, A. D., K. Englehart, B. Hudgins, and D. F. Lovely. Hidden Markov Model Classification of Myoelectric Signals in Speech. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada410037.

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Baggenstoss, Paul M. A Multi-Resolution Hidden Markov Model Using Class-Specific Features. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada494596.

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