Academic literature on the topic 'Hidden Markov process'
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Journal articles on the topic "Hidden Markov process"
Alshraideh, Hussam, and George Runger. "Process Monitoring Using Hidden Markov Models." Quality and Reliability Engineering International 30, no. 8 (September 2, 2013): 1379–87. http://dx.doi.org/10.1002/qre.1560.
Full textWang, Bing, Ping Yan, Qiang Zhou, and Libing Feng. "State recognition method for machining process of a large spot welder based on improved genetic algorithm and hidden Markov model." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 11 (January 27, 2016): 2135–46. http://dx.doi.org/10.1177/0954406215626942.
Full textXu, Yangsheng, and Ming Ge. "Hidden Markov model-based process monitoring system." Journal of Intelligent Manufacturing 15, no. 3 (June 2004): 337–50. http://dx.doi.org/10.1023/b:jims.0000026572.03164.64.
Full textElkimakh, Karima, and Abdelaziz Nasroallah. "Hidden Markov Model with Markovian emission." Monte Carlo Methods and Applications 26, no. 4 (December 1, 2020): 303–13. http://dx.doi.org/10.1515/mcma-2020-2072.
Full textYu, Feng-Hui, Wai-Ki Ching, Jia-Wen Gu, and Tak-Kuen Siu. "Interacting default intensity with a hidden Markov process." Quantitative Finance 17, no. 5 (November 7, 2016): 781–94. http://dx.doi.org/10.1080/14697688.2016.1237036.
Full textZuk, Or, Ido Kanter, and Eytan Domany. "The Entropy of a Binary Hidden Markov Process." Journal of Statistical Physics 121, no. 3-4 (November 2005): 343–60. http://dx.doi.org/10.1007/s10955-005-7576-y.
Full textKo, Stanley I. M., Terence T. L. Chong, and Pulak Ghosh. "Dirichlet Process Hidden Markov Multiple Change-point Model." Bayesian Analysis 10, no. 2 (June 2015): 275–96. http://dx.doi.org/10.1214/14-ba910.
Full textJacquet, Philippe, Gadiel Seroussi, and Wojciech Szpankowski. "On the entropy of a hidden Markov process." Theoretical Computer Science 395, no. 2-3 (May 2008): 203–19. http://dx.doi.org/10.1016/j.tcs.2008.01.012.
Full textWu, Hongmin, Yisheng Guan, and Juan Rojas. "Analysis of multimodal Bayesian nonparametric autoregressive hidden Markov models for process monitoring in robotic contact tasks." International Journal of Advanced Robotic Systems 16, no. 2 (March 1, 2019): 172988141983484. http://dx.doi.org/10.1177/1729881419834840.
Full textQi-feng, Yao, Dong Yun, and Wang Zhong-Zhi. "An Entropy Rate Theorem for a Hidden Inhomogeneous Markov Chain." Open Statistics & Probability Journal 8, no. 1 (September 30, 2017): 19–26. http://dx.doi.org/10.2174/1876527001708010019.
Full textDissertations / Theses on the topic "Hidden Markov process"
Jin, Chao. "A Sequential Process Monitoring Approach using Hidden Markov Model for Unobservable Process Drift." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445341969.
Full textMattila, Robert. "Hidden Markov models : Identification, control and inverse filtering." Licentiate thesis, KTH, Reglerteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-223683.
Full textQC 20180301
Chamroukhi, Faicel. "Hidden process regression for curve modeling, classification and tracking." Compiègne, 2010. http://www.theses.fr/2010COMP1911.
Full textThis research addresses the problem of diagnosis and monitoring for predictive maintenance of the railway infrastructure. In particular, the switch mechanism is a vital organ because its operating state directly impacts the overall safety of the railway system and its proper functioning is required for the full availability of the transportation system; monitoring it is a key task within maintenance team actions. To monitor and diagnose the switch mechanism, the main available data are curves of electric power acquired during several switch operations. This study therefore focuses on modeling curve-valued or functional data presenting regime changes. In this thesis we propose new probabilistic generative machine learning methodologies for curve modeling, classification, clustering and tracking. First, the models we propose for a single curve or independent sets of curves are based on specific regression models incorporating a flexible hidden process. They are able to capture non-stationary (dynamic) behavior within the curves and address the problem of missing information regarding the underlying regimes, and the problem of complex shaped classes. We then propose dynamic models for learning from curve sequences to make decision and prediction over time. The developed approaches rely on autoregressive dynamic models governed by hidden processes. The learning of the models is performed in both a batch mode (in which the curves are stored in advance) and an online mode as the learning proceeds (in which the curves are analyzed one at a time). The obtained results on both simulated curves and the real world switch operation curves demonstrate the practical use of the ideas introduced in this thesis
Balali, Samaneh. "Incorporating expert judgement into condition based maintenance decision support using a coupled hidden markov model and a partially observable markov decision process." Thesis, University of Strathclyde, 2012. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=19510.
Full textWong, Wee Chin. "Estimation and control of jump stochastic systems." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31775.
Full textCommittee Chair: Jay H. Lee; Committee Member: Alexander Gray; Committee Member: Erik Verriest; Committee Member: Magnus Egerstedt; Committee Member: Martha Grover; Committee Member: Matthew Realff. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Löhr, Wolfgang. "Models of Discrete-Time Stochastic Processes and Associated Complexity Measures." Doctoral thesis, Universitätsbibliothek Leipzig, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-38267.
Full textDamian, Camilla, Zehra Eksi-Altay, and Rüdiger Frey. "EM algorithm for Markov chains observed via Gaussian noise and point process information: Theory and case studies." De Gruyter, 2018. http://dx.doi.org/10.1515/strm-2017-0021.
Full textLöhr, Wolfgang. "Models of Discrete-Time Stochastic Processes and Associated Complexity Measures." Doctoral thesis, Max Planck Institut für Mathematik in den Naturwissenschaften, 2009. https://ul.qucosa.de/id/qucosa%3A11017.
Full textCarvalho, Walter Augusto Fonsêca de 1964. "Processos de renovação obtidos por agregação de estados a partir de um processo markoviano." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306196.
Full textTese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica
Made available in DSpace on 2018-08-24T12:54:22Z (GMT). No. of bitstreams: 1 Carvalho_WalterAugustoFonsecade_D.pdf: 1034671 bytes, checksum: 25dd72305f343655bedfde62a785a259 (MD5) Previous issue date: 2014
Resumo: Esta tese é dedicada ao estudo dos processos de renovação binários obtidos como agregação de estados a partir de processos Markovianos com alfabeto finito. Na primeira parte, utilizamos uma abordagem matricial para obter condições sob as quais o processo agregado pertence a cada uma das seguintes classes: (1) Markoviano de ordem finita, (2) processo de ordem infinita com probabilidades de transição contínuas, (3) processo Gibbsiano. A segunda parte trata da distância d entre processos de renovação binários. Obtivemos condições sob as quais esta distância pode ser atingida entre tais processos
Abstract: This thesis is devoted to the study of binary renewal processes obtained as aggregation of states from Markov processes with finite alphabet. In the rst part, we use a matrix approach to obtain conditions under which the aggregated process belongs to each of the following classes: (1) Markov of finite order, (2) process of infinite order with continuous transition probabilities, (3) Gibbsian process. The second part deals with the distance d between binary renewal processes. We obtain conditions under which this distance can be achieved between these processes
Doutorado
Estatistica
Doutor em Estatística
Starke, Martin, Benjamin Beck, Denis Ritz, Frank Will, and Jürgen Weber. "Frequency based efficiency evaluation - from pattern recognition via backwards simulation to purposeful drive design." Technische Universität Dresden, 2020. https://tud.qucosa.de/id/qucosa%3A71072.
Full textBooks on the topic "Hidden Markov process"
Eric, Moulines, and Rydén Tobias 1966-, eds. Inference in hidden Markov models. New York: Springer, 2005.
Find full textHidden Markov models for bioinformatics. Dordrecht: Kluwer Academic Publishers, 2001.
Find full textElliott, Robert J. Hidden Markov models: Estimation and control. New York: Springer-Verlag, 1995.
Find full textHidden Markov models and dynamical systems. Philadelphia: Society for Industrial and Applied Mathematics, 2008.
Find full textElliott, Robert J., and Rogemar S. Mamon. Hidden Markov models in finance. New York: Springer, 2011.
Find full textGrobel, Kirsti. Videobasierte Gebärdenspracherkennung mit Hidden-Markov-Modellen. Düsseldorf: VDI Verlag, 1999.
Find full textHuang, X. D. Hidden Markov models for speech recognition. Edinburgh: Edinburgh University Press, 1990.
Find full textGollery, Martin. Handbook of hidden Markov models in bioinformatics. Boca Raton: Chapman & Hall/CRC, 2008.
Find full textGollery, Martin. Handbook of hidden Markov models in bioinformatics. Boca Raton: CRC Press, 2008.
Find full textBhar, Ramaprasad. Hidden Markov models: Applications to financial economics. Boston, Mass: Kluwer Academic Publishers, 2004.
Find full textBook chapters on the topic "Hidden Markov process"
Visser, Ingmar, Maartje E. J. Raijmakers, and Han L. J. van der Maas. "Hidden Markov Models for Individual Time Series." In Dynamic Process Methodology in the Social and Developmental Sciences, 269–89. New York, NY: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-95922-1_13.
Full textZhou, Ding, Yuanjun Gao, and Liam Paninski. "Disentangled Sticky Hierarchical Dirichlet Process Hidden Markov Model." In Machine Learning and Knowledge Discovery in Databases, 612–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67658-2_35.
Full textCarrera, Berny, and Jae-Yoon Jung. "Constructing Probabilistic Process Models Based on Hidden Markov Models for Resource Allocation." In Business Process Management Workshops, 477–88. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15895-2_41.
Full textRoger, Vincent, Marius Bartcus, Faicel Chamroukhi, and Hervé Glotin. "Unsupervised Bioacoustic Segmentation by Hierarchical Dirichlet Process Hidden Markov Model." In Multimedia Tools and Applications for Environmental & Biodiversity Informatics, 113–30. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76445-0_7.
Full textAdomi, Masahiro, Yumi Shikauchi, and Shin Ishii. "Hidden Markov Model for Human Decision Process in a Partially Observable Environment." In Artificial Neural Networks – ICANN 2010, 94–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15822-3_12.
Full textManouchehri, Narges, and Nizar Bouguila. "Multivariate Beta-Based Hierarchical Dirichlet Process Hidden Markov Models in Medical Applications." In Unsupervised and Semi-Supervised Learning, 235–61. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99142-5_10.
Full textBaghdadi, Ali, Narges Manouchehri, Zachary Patterson, and Nizar Bouguila. "Shifted-Scaled Dirichlet-Based Hierarchical Dirichlet Process Hidden Markov Models with Variational Inference Learning." In Unsupervised and Semi-Supervised Learning, 263–92. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-030-99142-5_11.
Full textÖzyurt, I. Burak, Aydin K. Sunol, and Lawrence O. Hall. "Chemical process fault diagnosis using kernel retrofitted fuzzy genetic algorithm based learner (FGAL) with a hidden Markov model." In Lecture Notes in Computer Science, 190–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-64582-9_748.
Full textSachin Krishnan, P., K. Rameshkumar, and P. Krishnakumar. "Hidden Markov Modelling of High-Speed Milling (HSM) Process Using Acoustic Emission (AE) Signature for Predicting Tool Conditions." In Lecture Notes in Mechanical Engineering, 573–80. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1307-7_65.
Full textSperotto, Anna, Ramin Sadre, Pieter-Tjerk de Boer, and Aiko Pras. "Hidden Markov Model Modeling of SSH Brute-Force Attacks." In Integrated Management of Systems, Services, Processes and People in IT, 164–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04989-7_13.
Full textConference papers on the topic "Hidden Markov process"
Hamada, Ryunosuke, Takatomi Kubo, Kentaro Katahira, Kenta Suzuki, Kazuo Okanoya, and Kazushi Ikeda. "Birdsong analysis using beta process hidden Markov model." In 2014 IEEE 24th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2014. http://dx.doi.org/10.1109/mlsp.2014.6958848.
Full textPapavieros, George, Ioannis Kontoyiannis, Vassilios Constantoudis, and Evangelos Gogolides. "Denoising line edge roughness measurement using hidden Markov models." In Metrology, Inspection, and Process Control for Microlithography XXXIII, edited by Ofer Adan and Vladimir A. Ukraintsev. SPIE, 2019. http://dx.doi.org/10.1117/12.2523422.
Full textGao, Qing-Bin, and Shi-Liang Sun. "Human activity recognition with beta process hidden Markov models." In 2013 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2013. http://dx.doi.org/10.1109/icmlc.2013.6890353.
Full textKang, Yihuang, and Vladimir Zadorozhny. "Process Discovery Using Classification Tree Hidden Semi-Markov Model." In 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI). IEEE, 2016. http://dx.doi.org/10.1109/iri.2016.55.
Full textLi, Zhijun, Jiang Zhong, Cunwu Han, and Dehui Sun. "Process fault detection based on continuous hidden Markov model." In 2017 Chinese Automation Congress (CAC). IEEE, 2017. http://dx.doi.org/10.1109/cac.2017.8243244.
Full textKhodabandelou, Ghazaleh, Charlotte Hug, Rebecca Deneckere, and Camille Salinesi. "Supervised intentional process models discovery using Hidden Markov models." In 2013 IEEE Seventh International Conference on Research Challenges in Information Science (RCIS). IEEE, 2013. http://dx.doi.org/10.1109/rcis.2013.6577711.
Full textChoukri, Imane, Hatim Guermah, Abdelmajid Daosabah, and Mahmoud Nassar. "Context aware Hidden Markov Model for Intention process mining." In 2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS). IEEE, 2021. http://dx.doi.org/10.1109/icds53782.2021.9626765.
Full textYoon, Hyung-Jin, Donghwan Lee, and Naira Hovakimyan. "Hidden Markov Model Estimation-Based Q-learning for Partially Observable Markov Decision Process." In 2019 American Control Conference (ACC). IEEE, 2019. http://dx.doi.org/10.23919/acc.2019.8814849.
Full textChen, J., and C. J. Hsu. "A Self-Growing Hidden Markov Tree for Batch Process Monitoring." In 2007 2nd IEEE Conference on Industrial Electronics and Applications. IEEE, 2007. http://dx.doi.org/10.1109/iciea.2007.4318786.
Full textWang, Xiaofeng, Wei Ou, and Jinshu Su. "A reputation inference model based on linear hidden markov process." In 2009 ISECS International Colloquium on Computing, Communication, Control, and Management (CCCM). IEEE, 2009. http://dx.doi.org/10.1109/cccm.2009.5270424.
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