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 6. Februar 2022

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Zeitschriftenartikel zum Thema "Hidden Markov modelling"

Rasku, J., M. Juhola, T. Tossavainen, I. Pyykkö und E. Toppila. „Modelling stabilograms with hidden Markov models“. Journal of Medical Engineering & Technology 32, Nr. 4 (Januar 2008): 273–83. http://dx.doi.org/10.1080/03091900600968908.

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Rama, J. „Fuzzy Methods for Soft Hidden Markov Modelling“. International Journal for Research in Applied Science and Engineering Technology 6, Nr. 1 (31.01.2018): 23–31. http://dx.doi.org/10.22214/ijraset.2018.1005.

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Nkemnole, Edesiri Bridget, und Ekene Nwaokoro. „Modelling Customer Relationships as Hidden Markov Chains“. Path of Science 6, Nr. 11 (30.11.2020): 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 company by using an experimental data set. We develop and estimate an HMM to relate the unobservable relationship states to the observed buying behaviour of the customers giving an appropriate classification of the customers into the relationship states. By merely accounting for the functional and unobserved heterogeneity with a two-state hidden Markov model and taking estimation into account via an optimal estimation method, the empirical results not only demonstrate the value of the proposed model in assessing the dynamics of a customer relationship over time but also gives the optimal marketing-mixed strategies in different customer state.

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Koski, Antti. „Modelling ECG signals with hidden Markov models“. Artificial Intelligence in Medicine 8, Nr. 5 (Oktober 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, Nr. 1 (Januar 1995): 31–41. http://dx.doi.org/10.1016/0165-1684(94)00088-h.

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Whiting, J. P., M. F. Lambert und A. V. Metcalfe. „Modelling persistence in annual Australia point rainfall“. Hydrology and Earth System Sciences 7, Nr. 2 (30.04.2003): 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 by a hidden state Markov model. It is shown that conditioning the logarithm of rainfall on PDO, the Southern Oscillation index (SOI), and their interaction provides realistic simulation of rainfall that matches observed statistics. Similar simulation models are presented for Brisbane, Melbourne and Perth. Keywords: Hydrological persistence,hidden state Markov models, fractional differencing, PDO, SOI, Australian rainfall

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Chordia, Parag, Avinash Sastry und Sertan Şentürk. „Predictive Tabla Modelling Using Variable-length Markov and Hidden Markov Models“. Journal of New Music Research 40, Nr. 2 (Juni 2011): 105–18. http://dx.doi.org/10.1080/09298215.2011.576318.

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Segura, J. C., A. J. Rubio, A. M. Peinado, P. García und R. Román. „Multiple VQ hidden Markov modelling for speech recognition“. Speech Communication 14, Nr. 2 (April 1994): 163–70. http://dx.doi.org/10.1016/0167-6393(94)90006-x.

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Resnick, Sidney, und Ajay Subramanian. „Heavy tailed hidden semi-markov models“. Communications in Statistics. Stochastic Models 14, Nr. 1-2 (Januar 1998): 319–34. http://dx.doi.org/10.1080/15326349808807474.

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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 chain and other auxiliary processes related to the Markov chain are derived which in turn yield self-tuning dynamic financial models. A hidden Markov model (HMM)-based modelling set-up for commodity prices is developed and the predictability of the gold market under this setting is examined. An Ornstein-Uhlenbeck (OU) model with HMM parameters is proposed and under this set-up, we address two statistical inference issues: the sensitivity of the model to small changes in parameter estimates and the selection of the optimal number of states. The extended OU model is implemented on a data set of 30-day Canadian T-bill yields. An exponential of a Markov-switching OU process plus a compound Poisson process is put forward as a model for the evolution of electricity spot prices. Using a data set compiled by Nord Pool, we illustrate the vast improvements gained in incorporating regimes in the model. A multivariate HMM is employed as a framework in providing the solutions of two asset allocation problems; one involves the mean-variance utility function and the other entails the CVaR constraint. Finally, the valuation of credit default swaps highlights the important considerations necessitated by pricing in a regime-switching environment. Certain numerical schemes are applied to obtain approximations for the default probabilities and swap rates.

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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 Model and tracks changes in noise characteristics by a sequential model update procedure that tracks the noise characteristics during the absence of speech. In addition the algorithm will when necessary create new model states to represent novel noise spectra and will merge existing states that have similar characteristics. We then extend our work in robust noise estimation during speech activity by incorporating a speech model into our existing noise model. The noise characteristics within each state are updated based on a speech presence probability which is derived from a modified Minima controlled recursive averaging method. We have demonstrated the effectiveness of our noise HMM in tracking both stationary and highly non-stationary noise, and shown that it gives improved performance over other conventional noise estimation methods when it is incorporated into a standard speech enhancement algorithm.

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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 extended to pseudo two-dimensional HMMs, which offer a better and more flexible model, that describes some of the twodimensional dependencies missed by the standard one-dimensional model. It is shown how pseudo two-dimensional HMMs can be implemented, yielding successful recognition rates of up to around 95%. The performance of the HMMs is compared with the Eigenface approach and various domain and resolution experiments are also carried out. Finally, the performance of the HMM is evaluated in a fully automated system, where database images are cropped automatically.

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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 good models for simulated and real movement data. The mixtures of bivariate t distributions are shown to provide a robust parametric approach. Subsequently, we investigate several measures of overlap for assessing site delity in home range data. Di usion processes for home range data are considered to model the tracks of animals. In particular, we apply models based on a bivariate Ornstein-Uhlenbeck process to recorded coyote movements. We then study modelling in a di erent application area involving tracks. Di usion models for the movements of larvae are used to investigate their behaviour when exposed to chemical compounds in a scienti c study. We nd that the tted models represent the movements of the larvae well, and correctly distinguish between the behaviour of larvae exposed to attractant and repellent compounds. Mixtures of di usion processes and Hidden Markov models provide more exible alternatives to single di usion processes, and are found to improve upon them considerably. A Hidden Markov model with 4 states is determined to be optimal, with states accounting for directed movement, localized movement and stationary observations. Models incorporating higherorder dependence are investigated, but are found to be less e ective than the use of multiple states for modelling the larvae movements.

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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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Germain, Sarah Elizabeth. „Bayesian spatio-temporal modelling of rainfall through non-homogenous hidden Markov models“. Thesis, University of Newcastle Upon Tyne, 2010. http://hdl.handle.net/10443/1045.

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Multi-site statistical models for daily rainfall should account for spatial and temporal dependence amongst measurements and also allow for the event of no rain. Recent research into climate change and variability has sparked interest in the relationship between rainfall and climate, stimulating the development of statistical models that relate large-scale atmospheric variables to local precipitation. Although modelling daily rainfall presents a challenging and topical problem, there have been few attempts taking a subjective Bayesian approach. This thesis is concerned with developing hidden Markov models (HMMs) for the spatio-temporal analysis of rainfall data, within a Bayesian framework. In these models, daily rainfall patterns are driven by a finite number of unobserved states, interpreted as weather states, that evolve in time as a first order Markov chain. The weather states explain space time structure in the data so that reasonably simple models can be adopted within states. Throughout this thesis, the models and procedures are illustrated using data from a small dense network of six sites situated in Yorkshire, UK. First we study a simple (homogeneous) HMM in which rainfall occurrences and amounts, given occurrences, are conditionally independent in space and time, given the weather state, and have Bernoulli and gamma distributions, respectively. We compare methods for approximating the posterior distribution for the number of weather states. This simple model does not incorporate atmospheric information and appears not to capture the observed spatio-temporal structure. We therefore investigate two non-homogeneous hidden Markov models (NHMMs) in which we allow the transition probabilities between weather states to depend on time-varying atmospheric variables and successively relax the conditional independence assumptions. The first NHMM retains the simple conditional model for non-zero rainfall amounts but allows occurrences to form a Markov chain of autologistic models, given the weather state. The second introduces latent multivariate normal random variables to form a hierarchical NHMM in which neither rainfall occurrences nor non-zero amounts are conditionally spatially or temporally independent, given the weather state. Throughout this thesis, we emphasise the elicitation of prior distributions that convey genuine initial beliefs. For each hidden Markov model studied we demonstrate techniques to assist in this task.

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Bücher zum Thema "Hidden Markov modelling"

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

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Elliott, Robert J., Lakhdar Aggoun und 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. Wiesbaden: Vieweg+Teubner Verlag, 2003.

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Samuelsson, Christer. Statistical Methods. Herausgegeben von 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 evidence from several rules, each one of which alone might not be conclusive, but which taken together dramatically affect the probability. Maximum-entropy modelling allows combining heterogeneous information sources to produce a uniform probabilistic model where each piece of information is formulated as a feature. The key ideas of mathematical statistics are simple and intuitive, but tend to be buried in a sea of mathematical technicalities. Finally, the article provides mathematical detail related to the topic of discussion.

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Buchteile zum Thema "Hidden Markov modelling"

Vinciotti, Veronica. „Modelling ChIP-seq Data Using HMMs“. In Hidden Markov Models, 115–22. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6753-7_8.

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Fahrmeir, Ludwig, und Gerhard Tutz. „State Space and Hidden Markov Models“. In Multivariate Statistical Modelling Based on Generalized Linear Models, 331–83. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3454-6_8.

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Muri, Florence. „Modelling Bacterial Genomes Using Hidden Markov Models“. In COMPSTAT, 89–100. Heidelberg: Physica-Verlag HD, 1998. http://dx.doi.org/10.1007/978-3-662-01131-7_8.

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Koivisto, Mikko, Teemu Kivioja, Heikki Mannila, Pasi Rastas und Esko Ukkonen. „Hidden Markov Modelling Techniques for Haplotype Analysis“. In Lecture Notes in Computer Science, 37–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30215-5_4.

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Erlwein-Sayer, Christina, und Peter Ruckdeschel. „Robustification of an On-line EM Algorithm for Modelling Asset Prices Within an HMM“. In Hidden Markov Models in Finance, 1–31. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-1-4899-7442-6_1.

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Domańska, Joanna, Adam Domański und Tadeusz Czachórski. „Hidden Markov Models in Long Range Dependence Traffic Modelling“. In Communications in Computer and Information Science, 75–86. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66836-9_7.

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Derouault, Anne-Marie, und Bernard Merialdo. „Language modelling using a hidden Markov chain with application to automatic transcription of French stenotypy“. In Semi-Markov Models, 475–85. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-0574-1_29.

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Meffre, Alban, Christophe Collet, Nicolas Lachiche und Pierre Gançarski. „Real-Time Fall Detection Method Based on Hidden Markov Modelling“. In Lecture Notes in Computer Science, 521–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31254-0_59.

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Granat, Robert, und Andrea Donnellan. „A Hidden Markov Model Based Tool for Geophysical Data Exploration“. In Earthquake Processes: Physical Modelling, Numerical Simulation and Data Analysis Part II, 2271–83. Basel: Birkhäuser Basel, 2002. http://dx.doi.org/10.1007/978-3-0348-8197-5_7.

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White, Nicole M., Helen Johnson, Peter Silburn, Judith Rousseau und Kerrie L. Mengersen. „Hidden Markov Models for Complex Stochastic Processes: A Case Study in Electrophysiology“. In Case Studies in Bayesian Statistical Modelling and Analysis, 310–29. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118394472.ch18.

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Konferenzberichte zum Thema "Hidden Markov modelling"

Moon, T., und 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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Kazantzidis, Ioannis, Francisco Florez-Revuelta und 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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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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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 und 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. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3366030.3366092.

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Gales, M. J. F. „Acoustic modelling for speech recognition: Hidden Markov models and beyond?“ In Understanding (ASRU). IEEE, 2009. http://dx.doi.org/10.1109/asru.2009.5372953.

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Chattopadhyay, Sourav, und 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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Slimane, Fouad, Rolf Ingold, Adel M. Alimi und Jean Hennebert. „Duration Models for Arabic Text Recognition Using Hidden Markov Models“. In 2008 International Conference on Computational Intelligence for Modelling Control & Automation. IEEE, 2008. http://dx.doi.org/10.1109/cimca.2008.229.

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Xin, Ji-rong, Yan Zhao und Laiyuan Luo. „Detection of bearing-only trajectory of multi-target using Hidden Markov model“. In 2017 9th International Conference on Modelling, Identification and Control (ICMIC). IEEE, 2017. http://dx.doi.org/10.1109/icmic.2017.8321564.

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Almeida, Gustavo Matheus de, und Song Won Park. „Fault Detection in Continuous and Periodic Industrial Chemical Processes with Hidden Markov Models“. In Modelling, Simulation and Identification / 841: Intelligent Systems and Control. Calgary,AB,Canada: ACTAPRESS, 2016. http://dx.doi.org/10.2316/p.2016.840-059.

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Berichte der Organisationen zum Thema "Hidden Markov modelling"

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

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