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Artykuły w czasopismach na temat "Hiden Markov model":
Kim, Sae-Joong, Young-Han Jung i Chong-Kwan Heo. "Analysis sports using the Hidden Markov Model". Korean Journal of Sports Science 26, nr 3 (30.06.2017): 1301–9. http://dx.doi.org/10.35159/kjss.2017.06.26.3.1301.
Lay, Khin Khin, i Aung Cho. "Myanmar Named Entity Recognition with Hidden Markov Model". International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (30.06.2019): 1144–47. http://dx.doi.org/10.31142/ijtsrd24012.
Buckby, Jodie, Ting Wang, Jiancang Zhuang i Kazushige Obara. "Model Checking for Hidden Markov Models". Journal of Computational and Graphical Statistics 29, nr 4 (14.05.2020): 859–74. http://dx.doi.org/10.1080/10618600.2020.1743295.
Ghods, Vahid, i Mohammad Karim Sohrabi. "Online Farsi Handwritten Character Recognition Using Hidden Markov Model". Journal of Computers 11, nr 2 (marzec 2016): 169–75. http://dx.doi.org/10.17706/jcp.11.2.169-175.
Bhatia, Varsha. "Applications of Hidden Markov Model in Wireless Sensor Network". International Journal of Psychosocial Rehabilitation 24, nr 4 (30.04.2020): 6549–57. http://dx.doi.org/10.37200/ijpr/v24i4/pr2020465.
Ye, Fei, i 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.
Grewal, Jasleen K., Martin Krzywinski i Naomi Altman. "Markov models — hidden Markov models". Nature Methods 16, nr 9 (30.08.2019): 795–96. http://dx.doi.org/10.1038/s41592-019-0532-6.
Tumilaar, Kezia, Yohanes Langi i Altien Rindengan. "Hidden Markov Model". d'CARTESIAN 4, nr 1 (10.02.2015): 86. http://dx.doi.org/10.35799/dc.4.1.2015.8104.
Lee, Kyung-Ah, Dae-Jong Lee, Jang-Hwan Park i Myung-Geun Chun. "Face Recognition Using Wavelet Coefficients and Hidden Markov Model". Journal of Korean Institute of Intelligent Systems 13, nr 6 (1.12.2003): 673–78. http://dx.doi.org/10.5391/jkiis.2003.13.6.673.
Johansson, Mathias, i Tomas Olofsson. "Bayesian Model Selection for Markov, Hidden Markov, and Multinomial Models". IEEE Signal Processing Letters 14, nr 2 (luty 2007): 129–32. http://dx.doi.org/10.1109/lsp.2006.882094.
Rozprawy doktorskie na temat "Hiden Markov model":
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.
Cropping management practices is an agronomic notion grasping the interdependence between targeted yield and input use levels. Subsequently, one can legitimately assume that different cropping management practices are associated to different production functions. To better understand pesticide dependence – a key point to encourage more sustainable practices – one have to consider modelling cropping management practices specific production functions.Because of the inherent interdependence between those practices and their associeted yield and input use levels, we need to consider endogenous regime switching models.When unobserved, the sequence of cropping management practices choices is considered as a Markovian process. From this modelling framework we can derive the cropping management choices, their dynamics, their associated yield and input use levels. When observed, we consider primal production functions to see how yield responds differently to input uses based on the different cropping management practices. Thus, we can assess jointly the effect of a public policy on input use and yield levels.In a nutshell, in this PhD we are aiming at giving some tools to evaluate the differentiated effect of agri-environmental public policies on production choies and on the associated yield and input use levels
Kotsalis, Georgios. "Model reduction for Hidden Markov models". Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38255.
Includes bibliographical references (leaves 57-60).
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 based on randomization of the parametric input is introduced. The main point of the reduction algorithm lies in the formulation of two dissipation inequalities, which in conjunction with a suitably defined storage function enable the derivation of low complexity models, whose fidelity is controlled by a guaranteed upper bound on the stochastic L2 gain of the approximation error. The developed reduction procedure can be interpreted as an extension of the balanced truncation method to the broader class of Jump Linear Systems. In the Hidden Markov Model case, Hidden Markov Models are identified with appropriate Jump Linear Systems that satisfy certain constraints on the coefficients of the linear transformation. This correspondence enables the development of a two step reduction procedure.
(cont.) In the first step, the image of the high dimensional Hidden Markov Model in the space of Jump Linear Systems is simplified by means of the aforementioned balanced truncation method. Subsequently, in the second step, the constraints that reflect the Hidden Markov Model structure are imposed by solving a low dimensional non convex optimization problem. Numerical simulation results provide evidence that the proposed algorithm computes accurate reduced order Hidden Markov Models, while achieving a compression of the state space by orders of magnitude.
by Georgios Kotsalis.
Ph.D.
Kadhem, Safaa K. "Model fit diagnostics for hidden Markov models". Thesis, University of Plymouth, 2017. http://hdl.handle.net/10026.1/9966.
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.
Wynne-Jones, Michael. "Model building in neural networks with hidden Markov models". Thesis, University of Edinburgh, 1994. http://hdl.handle.net/1842/284.
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.
Under de senaste decennierna har det gjorts stora framsteg inom finansiell teori för kapitalmarknader. Formuleringen av arbitrageteori medförde möjligheten att konsekvent kunna prissätta finansiella instrument. Men i en tid då högfrekvenshandel numera är standard, har omsättningen av information i pris börjat ske i allt snabbare takt. För att studera dessa fenomen; prispåverkan och informationsomsättning, har mikrostrukturteorin vuxit fram. I den här uppsatsen studerar vi mikrostruktur med hjälp av en dynamisk modell. Historiskt sett har mikrostrukturteorin fokuserat på statiska modeller men med hjälp av icke-linjära dolda Markovmodeller (HMM:er) utökar vi detta till den dynamiska domänen. HMM:er kommer med en naturlig uppdelning mellan observation och dynamik, och är utformade på ett sådant sätt att vi kan dra nytta av domänspecifik kunskap. Genom att formulera lämpliga nyckelantaganden baserade på traditionell mikrostrukturteori specificerar vi en modell—med endast ett fåtal parametrar—som klarar av att beskriva de välkända säsongsbeteenden som statiska modeller inte klarar av. Tack vare nya genombrott inom Monte Carlo-metoder finns det nu kraftfulla verktyg att tillgå för att utföra optimal filtrering med HMM:er i realtid. Vi applicerar ett så kallat bootstrap filter för att sekventiellt filtrera fram tillståndet för modellen och prediktera framtida tillstånd. Tillsammans med tekniken backward smoothing estimerar vi den posteriora simultana fördelningen för varje handelsdag. Denna används sedan för statistisk inlärning av våra hyperparametrar via en sekventiell Monte Carlo Expectation Maximization-algoritm. För att formulera en modell som beskriver omsättningen av information, väljer vi att utgå ifrån volume imbalance, som ofta används för att studera prispåverkan. Vi definierar den relaterade observerbara storheten scaled volume imbalance som syftar till att bibehålla kopplingen till prispåverkan men även går att modellera med en dynamisk process som passar in i ramverket för HMM:er. Vi visar även hur man inom detta ramverk kan utvärdera HMM:er i allmänhet, samt genomför denna analys för vår modell i synnerhet. Modellen testas mot finansiell handelsdata för både terminskontrakt och aktier och visar i bägge fall god predikteringsförmåga.
Hofer, Gregor Otto. "Speech-driven animation using multi-modal hidden Markov models". Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/3786.
McKee, Bill Frederick. "Optimal hidden Markov models". Thesis, University of Plymouth, 1999. http://hdl.handle.net/10026.1/1698.
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.
Includes bibliographical references (leaves 119-123). Also available in electronic version. Access restricted to campus users.
Schimert, James. "A high order hidden Markov model /". Thesis, Connect to this title online; UW restricted, 1992. http://hdl.handle.net/1773/8939.
Książki na temat "Hiden Markov model":
Westhead, David R., i M. S. Vijayabaskar, red. Hidden Markov Models. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6753-7.
Cappé, Olivier. Inference in hidden Markov models. New York: Springer, 2005.
Cappe, Olivier. Inference in hidden Markov models. New York, NY: Springer, 2005.
Koski, Timo. Hidden Markov models for bioinformatics. Dordrecht: Kluwer Academic Publishers, 2001.
Elliott, Robert J., i Rogemar S. Mamon. Hidden Markov models in finance. New York: Springer, 2011.
Koski, Timo. Hidden Markov Models for Bioinformatics. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0612-5.
Mamon, Rogemar S., i Robert J. Elliott, red. Hidden Markov Models in Finance. Boston, MA: Springer US, 2007. http://dx.doi.org/10.1007/0-387-71163-5.
Cappé, Olivier, Eric Moulines i Tobias Rydén. Inference in Hidden Markov Models. New York, NY: Springer New York, 2005. http://dx.doi.org/10.1007/0-387-28982-8.
Mamon, Rogemar S., i Robert J. Elliott, red. Hidden Markov Models in Finance. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-1-4899-7442-6.
Bouguila, Nizar, Wentao Fan i Manar Amayri, red. Hidden Markov Models and Applications. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99142-5.
Części książek na temat "Hiden Markov model":
Awad, Mariette, i Rahul Khanna. "Hidden Markov Model". W Efficient Learning Machines, 81–104. Berkeley, CA: Apress, 2015. http://dx.doi.org/10.1007/978-1-4302-5990-9_5.
Wu, Jun. "Hidden Markov model". W The Beauty of Mathematics in Computer Science, 43–51. Boca Raton, FL : Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315169491-5.
Sucar, Luis Enrique. "Hidden Markov Models". W Probabilistic Graphical Models, 63–82. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6699-3_5.
Sucar, Luis Enrique. "Hidden Markov Models". W Probabilistic Graphical Models, 71–91. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61943-5_5.
Coelho, João Paulo, Tatiana M. Pinho i José Boaventura-Cunha. "Autoregressive Markov Models". W Hidden Markov Models, 207–43. Boca Raton, FL : CRC Press, 2019. | “A science publishers book.”: CRC Press, 2019. http://dx.doi.org/10.1201/9780429261046-5.
Coelho, João Paulo, Tatiana M. Pinho i José Boaventura-Cunha. "Discrete Hidden Markov Models". W Hidden Markov Models, 29–155. Boca Raton, FL : CRC Press, 2019. | “A science publishers book.”: CRC Press, 2019. http://dx.doi.org/10.1201/9780429261046-3.
Coelho, João Paulo, Tatiana M. Pinho i José Boaventura-Cunha. "Continuous Hidden Markov Models". W Hidden Markov Models, 157–205. Boca Raton, FL : CRC Press, 2019. | “A science publishers book.”: CRC Press, 2019. http://dx.doi.org/10.1201/9780429261046-4.
Fink, Gernot A. "Hidden Markov Models". W Markov Models for Pattern Recognition, 71–106. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6308-4_5.
Ephraim, Yariv. "Hidden Markov Models". W Encyclopedia of Operations Research and Management Science, 704–8. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_417.
Hernando, Javier. "Hidden Markov Models". W Encyclopedia of Biometrics, 702–7. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_195.
Streszczenia konferencji na temat "Hiden Markov model":
Farshchian, Maryam, i Majid Vafaei Jahan. "Stock market prediction with Hidden Markov Model". W 2015 International Congress on Technology, Communication and Knowledge (ICTCK). IEEE, 2015. http://dx.doi.org/10.1109/ictck.2015.7582714.
Somani, Poonam, Shreyas Talele i Suraj Sawant. "Stock market prediction using Hidden Markov Model". W 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). IEEE, 2014. http://dx.doi.org/10.1109/itaic.2014.7065011.
Gupta, Aditya, i Bhuwan Dhingra. "Stock market prediction using Hidden Markov Models". W 2012 Students Conference on Engineering and Systems (SCES). IEEE, 2012. http://dx.doi.org/10.1109/sces.2012.6199099.
Gupta, Kriti. "Hidden Markov Model". W the International Conference & Workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1980022.1980326.
Ben Ayed, Alaidine, i S. Selouani. "Market customers classification using Hidden Markov Models toolkit". W 2013 International Conference on Computer Applications Technology (ICCAT 2013). IEEE, 2013. http://dx.doi.org/10.1109/iccat.2013.6521974.
Dimoulkas, Ilias, Mikael Amelin i Mohammad Reza Hesamzadeh. "Forecasting balancing market prices using Hidden Markov Models". W 2016 13th International Conference on the European Energy Market (EEM). IEEE, 2016. http://dx.doi.org/10.1109/eem.2016.7521229.
Kotsalis, Georgios, Alexandre Megretski i Munther A. Dahleh. "A Model Reduction Algorithm for Hidden Markov Models". W Proceedings of the 45th IEEE Conference on Decision and Control. IEEE, 2006. http://dx.doi.org/10.1109/cdc.2006.377011.
Hassan, M. R., i B. Nath. "Stock market forecasting using hidden Markov model: a new approach". W 5th International Conference on Intelligent Systems Design and Applications (ISDA'05). IEEE, 2005. http://dx.doi.org/10.1109/isda.2005.85.
Radenen, Mathieu, i Thierry Artieres. "Contextual Hidden Markov Models". W ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288328.
"15 - Hidden Markov Models". W 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628684.
Raporty organizacyjne na temat "Hiden Markov model":
Ghahramani, Zoubin, i Michael I. Jordan. Factorial Hidden Markov Models. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1996. http://dx.doi.org/10.21236/ada307097.
Ainsleigh, Phillip L. Theory of Continuous-State Hidden Markov Models and Hidden Gauss-Markov Models. Fort Belvoir, VA: Defense Technical Information Center, marzec 2001. http://dx.doi.org/10.21236/ada415930.
Yang, Jie, i Yangsheng Xu. Hidden Markov Model for Gesture Recognition. Fort Belvoir, VA: Defense Technical Information Center, maj 1994. http://dx.doi.org/10.21236/ada282845.
Thrun, Sebastian, i John Langford. Monte Carlo Hidden Markov Models. Fort Belvoir, VA: Defense Technical Information Center, grudzień 1998. http://dx.doi.org/10.21236/ada363714.
Hollis, Andrew, George Tompkins, Alyson Wilson i Ralph Smith. Proliferation Monitoring with Hidden Markov Models. Office of Scientific and Technical Information (OSTI), luty 2021. http://dx.doi.org/10.2172/1766975.
Del Rose, Michael S., Philip Frederick i Christian Wagner. Using Evidence Feed-Forward Hidden Markov Models. Fort Belvoir, VA: Defense Technical Information Center, maj 2010. http://dx.doi.org/10.21236/ada543331.
Balasubramanian, Vijay. Equivalence and Reduction of Hidden Markov Models. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1993. http://dx.doi.org/10.21236/ada270762.
Chan, A. D., K. Englehart, B. Hudgins i D. F. Lovely. Hidden Markov Model Classification of Myoelectric Signals in Speech. Fort Belvoir, VA: Defense Technical Information Center, październik 2001. http://dx.doi.org/10.21236/ada410037.
Baggenstoss, Paul M. A Multi-Resolution Hidden Markov Model Using Class-Specific Features. Fort Belvoir, VA: Defense Technical Information Center, styczeń 2008. http://dx.doi.org/10.21236/ada494596.
Dey, Subhrakanti, i Steven I. Marcus. A Framework for Mixed Estimation of Hidden Markov Models. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1998. http://dx.doi.org/10.21236/ada438575.