Thematische Bibliographien / Hidden Markov modelling

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Auswahl der wissenschaftlichen Literatur zum Thema „Hidden Markov modelling“

Autor: Grafiati

Veröffentlicht am 26. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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Zeitschriftenartikel zum Thema "Hidden Markov modelling"

Rasku, J., M. Juhola, T. Tossavainen, I. Pyykkö, and E. Toppila. "Modelling stabilograms with hidden Markov models." Journal of Medical Engineering & Technology 32, no. 4 (2008): 273–83. http://dx.doi.org/10.1080/03091900600968908.

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Whiting, J. P., M. F. Lambert, and A. V. Metcalfe. "Modelling persistence in annual Australia point rainfall." Hydrology and Earth System Sciences 7, no. 2 (2003): 197–211. http://dx.doi.org/10.5194/hess-7-197-2003.

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Abstract. Annual rainfall time series for Sydney from 1859 to 1999 is analysed. Clear evidence of nonstationarity is presented, but substantial evidence for persistence or hidden states is more elusive. A test of the hypothesis that a hidden state Markov model reduces to a mixture distribution is presented. There is strong evidence of a correlation between the annual rainfall and climate indices. Strong evidence of persistence of one of these indices, the Pacific Decadal Oscillation (PDO), is presented together with a demonstration that this is better modelled by fractional differencing than b

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Rimella, Lorenzo, and Nick Whiteley. "Hidden Markov Neural Networks." Entropy 27, no. 2 (2025): 168. https://doi.org/10.3390/e27020168.

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We define an evolving in-time Bayesian neural network called a Hidden Markov Neural Network, which addresses the crucial challenge in time-series forecasting and continual learning: striking a balance between adapting to new data and appropriately forgetting outdated information. This is achieved by modelling the weights of a neural network as the hidden states of a Hidden Markov model, with the observed process defined by the available data. A filtering algorithm is employed to learn a variational approximation of the evolving-in-time posterior distribution over the weights. By leveraging a s

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Borucka, Anna, Edward Kozłowski, Rafał Parczewski, Katarzyna Antosz, Leszek Gil, and Daniel Pieniak. "Supply Sequence Modelling Using Hidden Markov Models." Applied Sciences 13, no. 1 (2022): 231. http://dx.doi.org/10.3390/app13010231.

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Logistics processes, their effective planning as well as proper management and effective implementation are of key importance in an enterprise. This article analyzes the process of supplying raw materials necessary for the implementation of production tasks. The specificity of the examined waste processing company requires the knowledge about the size of potential deliveries because the delivered waste must be properly managed and stored due to its toxicity to the natural environment. In the article, hidden Markov models were used to assess the level of supply. They are a statistical modeling

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Nkemnole, Edesiri Bridget, and Ekene Nwaokoro. "Modelling Customer Relationships as Hidden Markov Chains." Path of Science 6, no. 11 (2020): 5011–19. http://dx.doi.org/10.22178/pos.64-9.

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Models in behavioural relationship marketing suggest that relations between the customer and the company change over time as a result of the continuous encounter. Some theoretical models have been put forward concerning relationship marketing, both from the standpoints of consumer behaviour and empirical modelling. In addition to these, this study proposes the hidden Markov model (HMM) as a potential tool for assessing customer relationships. Specifically, the HMM is submitted via the framework of a Markov chain model to classify customers relationship dynamics of a telecommunication service c

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Rama, J. "Fuzzy Methods for Soft Hidden Markov Modelling." International Journal for Research in Applied Science and Engineering Technology 6, no. 1 (2018): 23–31. http://dx.doi.org/10.22214/ijraset.2018.1005.

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Koski, Antti. "Modelling ECG signals with hidden Markov models." Artificial Intelligence in Medicine 8, no. 5 (1996): 453–71. http://dx.doi.org/10.1016/s0933-3657(96)00352-1.

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Baran, Robert H. "A software package for hidden Markov modelling." Mathematical and Computer Modelling 11 (1988): 476–80. http://dx.doi.org/10.1016/0895-7177(88)90538-9.

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Vaseghi, S. V. "State duration modelling in hidden Markov models." Signal Processing 41, no. 1 (1995): 31–41. http://dx.doi.org/10.1016/0165-1684(94)00088-h.

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George, Sebastian, and Ambily Jose. "Generalized Poisson Hidden Markov Model for Overdispersed or Underdispersed Count Data." Revista Colombiana de Estadística 43, no. 1 (2020): 71–82. http://dx.doi.org/10.15446/rce.v43n1.77542.

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The most suitable statistical method for explaining serial dependency in time series count data is that based on Hidden Markov Models (HMMs). These models assume that the observations are generated from a finite mixture of distributions governed by the principle of Markov chain (MC). Poisson-Hidden Markov Model (P-HMM) may be the most widely used method for modelling the above said situations. However, in real life scenario, this model cannot be considered as the best choice. Taking this fact into account, we, in this paper, go for Generalised Poisson Distribution (GPD) for modelling count dat

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Dissertationen zum Thema "Hidden Markov modelling"

Erlwein, Christina. "Applications of hidden Markov models in financial modelling." Thesis, Brunel University, 2008. http://bura.brunel.ac.uk/handle/2438/7898.

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Various models driven by a hidden Markov chain in discrete or continuous time are developed to capture the stylised features of market variables whose levels or values constitute as the underliers of financial derivative contracts or investment portfolios. Since the parameters are switching regimes, the changes and developments in the economy as soon as they arise are readily reflected in these models. The change of probability measure technique and the EM algorithm are fundamental techniques utilised in the optimal parameter estimation. Recursive adaptive filters for the state of the Markov c

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Bai, Jiongjun. "Adaptive hidden Markov noise modelling for speech enhancement." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/11158.

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A robust and reliable noise estimation algorithm is required in many speech enhancement systems. The aim of this thesis is to propose and evaluate a robust noise estimation algorithm for highly non-stationary noisy environments. In this work, we model the non-stationary noise using a set of discrete states with each state representing a distinct noise power spectrum. In this approach, the state sequence over time is conveniently represented by a Hidden Markov Model (HMM). In this thesis, we first present an online HMM re-estimation framework that models time-varying noise using a Hidden Markov

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Ljolje, A. "Intonation and phonetic segmentation using hidden Markov models." Thesis, University of Cambridge, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.377219.

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Samaria, Ferdinando Silvestro. "Face recognition using Hidden Markov Models." Thesis, University of Cambridge, 1995. https://www.repository.cam.ac.uk/handle/1810/244871.

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This dissertation introduces work on face recognition using a novel technique based on Hidden Markov Models (HMMs). Through the integration of a priori structural knowledge with statistical information, HMMs can be used successfully to encode face features. The results reported are obtained using a database of images of 40 subjects, with 5 training images and 5 test images for each. It is shown how standard one-dimensional HMMs in the shape of top-bottom models can be parameterised, yielding successful recognition rates of up to around 85%. The insights gained from top-bottom models are extend

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Wong, Georges. "Improved speech hidden Markov modelling via an expectation-maximization framework." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259544.

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McLellan, Christopher Richard. "Statistical modelling of home range and larvae movement data." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/14202.

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In this thesis, we investigate two di erent approaches to animal movement modelling; nite mixture models, and di usion processes. These models are considered in two di erent contexts, rstly for analysis of data obtained in home range studies, and then, on a much smaller scale, modelling the movements of larvae. We consider the application of mixture models to home range movement data, and compare their performance with kernel density estimators commonly used for this purpose. Mixtures of bivariate normal distributions and bivariate t distributions are considered, and the latter are found to be

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Zhang, X. "A semi-hidden Markov model and its application to speech recognition." Thesis, Swansea University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378829.

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Leggetter, Christopher John. "Improved acoustic modelling for HMMs using linear transformations." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361709.

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Henderson, Daniel Adrian. "Modelling and analysis of non-coding DNA sequence data." Thesis, University of Newcastle Upon Tyne, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299427.

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Chis, Tiberiu. "Performance modelling with adaptive hidden Markov models and discriminatory processor sharing queues." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/39049.

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In modern computer systems, workload varies at different times and locations. It is important to model the performance of such systems via workload models that are both representative and efficient. For example, model-generated workloads represent realistic system behaviour, especially during peak times, when it is crucial to predict and address performance bottlenecks. In this thesis, we model performance, namely throughput and delay, using adaptive models and discrete queues. Hidden Markov models (HMMs) parsimoniously capture the correlation and burstiness of workloads with spatiotemporal ch

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Bücher zum Thema "Hidden Markov modelling"

Grobel, Kirsti. Videobasierte Gebärdenspracherkennung mit Hidden-Markov-Modellen. VDI Verlag, 1999.

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Oberle, Stefan. Detektion und Estimation von akustischen Signalen mit Hidden-Markov-Modellen. [s.n.], 1998.

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Elliott, Robert J., Lakhdar Aggoun, and John B. Moore. Hidden Markov Models: Estimation and Control (Stochastic Modelling and Applied Probability). Springer, 1997.

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Mustererkennung mit Markov-Modellen: Theorie -- Praxis -- Anwendungsgebiete. Vieweg+Teubner Verlag, 2003.

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Samuelsson, Christer. Statistical Methods. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0019.

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Statistical methods now belong to mainstream natural language processing. They have been successfully applied to virtually all tasks within language processing and neighbouring fields, including part-of-speech tagging, syntactic parsing, semantic interpretation, lexical acquisition, machine translation, information retrieval, and information extraction and language learning. This article reviews mathematical statistics and applies it to language modelling problems, leading up to the hidden Markov model and maximum entropy model. The real strength of maximum-entropy modelling lies in combining

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Bieler, Kaspar. Vergleich von Multilayer Perzeptron: Kohonen Maps und Hidden Markov Modellen für die Fehlerdiagnose in Elektrischen Energieübertragungssystemen. 1994.

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Buchteile zum Thema "Hidden Markov modelling"

Møller, Jan Kloppenborg, Marcel Schweiker, Rune Korsholm Andersen, et al. "Hidden Markov models." In Statistical Modelling of Occupant Behaviour. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003340812-11.

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Agbinya, Johnson I. "Hidden Markov Modelling (HMM)." In Applied Data Analytics - Principles and Applications. River Publishers, 2022. http://dx.doi.org/10.1201/9781003337225-2.

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Vinciotti, Veronica. "Modelling ChIP-seq Data Using HMMs." In Hidden Markov Models. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6753-7_8.

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Senthamarai Kannan, K., and S. D. Jeniffer. "Hidden Markov Modelling for Biological Sequence." In Proceedings of International Conference on Computational Intelligence. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2126-1_31.

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Fahrmeir, Ludwig, and Gerhard Tutz. "State Space and Hidden Markov Models." In Multivariate Statistical Modelling Based on Generalized Linear Models. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3454-6_8.

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Koivisto, Mikko, Teemu Kivioja, Heikki Mannila, Pasi Rastas, and Esko Ukkonen. "Hidden Markov Modelling Techniques for Haplotype Analysis." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30215-5_4.

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Muri, Florence. "Modelling Bacterial Genomes Using Hidden Markov Models." In COMPSTAT. Physica-Verlag HD, 1998. http://dx.doi.org/10.1007/978-3-662-01131-7_8.

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Elings, Jelger, Rachel Mawer, Matthias Schneider, et al. "Applying Hidden Markov Modelling to Fine-Scale Telemetry." In Advances in Hydraulic Research. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56093-4_8.

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Erlwein-Sayer, Christina, and Peter Ruckdeschel. "Robustification of an On-line EM Algorithm for Modelling Asset Prices Within an HMM." In Hidden Markov Models in Finance. Springer US, 2014. http://dx.doi.org/10.1007/978-1-4899-7442-6_1.

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Domańska, Joanna, Adam Domański, and Tadeusz Czachórski. "Hidden Markov Models in Long Range Dependence Traffic Modelling." In Communications in Computer and Information Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66836-9_7.

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Konferenzberichte zum Thema "Hidden Markov modelling"

He, Xiangbin, Wenhui Ding, Guiling Fan, Yanqin Yin, and Tianli Li. "Multistep time series prediction method integrating hidden Markov model and deep reinforcement learning." In 2025 5th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2025), edited by Peicheng Zhu and Guihua Lin. SPIE, 2025. https://doi.org/10.1117/12.3070373.

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Perantoni, Gianmarco, Giulio Weikmann, and Lorenzo Bruzzone. "Bayesian Modelling of Multi-Year Crop Type Classification Using Deep Neural Networks and Hidden Markov Models." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642432.

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Toleti, Jayant, Soumyendra Singh, and R. Prasanna Kumar. "Developing Robust Crypto-Currency Trading Strategies: A Fusion of Machine Learning and Hidden Markov Modelling for Enhanced Performance." In 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2024. http://dx.doi.org/10.1109/conecct62155.2024.10677046.

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Preez, J. A. du, and D. M. Weber. "Efficient high-order hidden Markov modelling." In 5th International Conference on Spoken Language Processing (ICSLP 1998). ISCA, 1998. http://dx.doi.org/10.21437/icslp.1998-173.

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Moon, T., and T. Weissman. "Discrete universal filtering via hidden Markov modelling." In Proceedings. International Symposium on Information Theory, 2005. ISIT 2005. IEEE, 2005. http://dx.doi.org/10.1109/isit.2005.1523549.

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Rezek, L. "Coupled hidden Markov models for biosignal interaction modelling." In First International Conference on Advances in Medical Signal and Information Processing. IEE, 2000. http://dx.doi.org/10.1049/cp:20000317.

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Kazantzidis, Ioannis, Francisco Florez-Revuelta, and Jean-Christophe Nebel. "Profile Hidden Markov Models for Foreground Object Modelling." In 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018. http://dx.doi.org/10.1109/icip.2018.8451476.

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N.A., Lili. "Hidden Markov Model for Content-Based Video Retrieval." In 2009 Third Asia International Conference on Modelling & Simulation (AMS 2009). IEEE, 2009. http://dx.doi.org/10.1109/ams.2009.24.

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Naskar, Debashis, Eva Onaindia, Miguel Rebollo, and Subhashis Das. "Modelling Emotion Dynamics on Twitter via Hidden Markov Model." In iiWAS2019: The 21st International Conference on Information Integration and Web-based Applications & Services. ACM, 2019. http://dx.doi.org/10.1145/3366030.3366092.

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Chattopadhyay, Sourav, and Anup Nandy. "Human Gait Modelling Using Hidden Markov Model For Abnormality Detection." In TENCON 2018 - 2018 IEEE Region 10 Conference. IEEE, 2018. http://dx.doi.org/10.1109/tencon.2018.8650194.

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Berichte der Organisationen zum Thema "Hidden Markov modelling"

Streso, Katy, and Francesco Lagona. Hidden Markov random field and FRAME modelling for TCA-image analysis. Max Planck Institute for Demographic Research, 2005. http://dx.doi.org/10.4054/mpidr-wp-2005-032.

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