Relevant bibliographies by topics / Hidden Markov Models

Academic literature on the topic 'Hidden Markov Models'

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Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Hidden Markov Models"

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Grewal, Jasleen K., Martin Krzywinski, and Naomi Altman. "Markov models — hidden Markov models." Nature Methods 16, no. 9 (August 30, 2019): 795–96. http://dx.doi.org/10.1038/s41592-019-0532-6.

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Eddy, Sean R. "Hidden Markov models." Current Opinion in Structural Biology 6, no. 3 (June 1996): 361–65. http://dx.doi.org/10.1016/s0959-440x(96)80056-x.

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Adams, Stephen, Peter A. Beling, and Randy Cogill. "Feature Selection for Hidden Markov Models and Hidden Semi-Markov Models." IEEE Access 4 (2016): 1642–57. http://dx.doi.org/10.1109/access.2016.2552478.

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Kersting, K., L. De Raedt, and T. Raiko. "Logical Hidden Markov Models." Journal of Artificial Intelligence Research 25 (April 19, 2006): 425–56. http://dx.doi.org/10.1613/jair.1675.

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Logical hidden Markov models (LOHMMs) upgrade traditional hidden Markov models to deal with sequences of structured symbols in the form of logical atoms, rather than flat characters. This note formally introduces LOHMMs and presents solutions to the three central inference problems for LOHMMs: evaluation, most likely hidden state sequence and parameter estimation. The resulting representation and algorithms are experimentally evaluated on problems from the domain of bioinformatics.
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Forchhammer, S., and J. Rissanen. "Partially hidden Markov models." IEEE Transactions on Information Theory 42, no. 4 (July 1996): 1253–56. http://dx.doi.org/10.1109/18.508852.

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Altman, Rachel MacKay. "Mixed Hidden Markov Models." Journal of the American Statistical Association 102, no. 477 (March 2007): 201–10. http://dx.doi.org/10.1198/016214506000001086.

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Barrett, Christian, Richard Hughey, and Kevin Karplus. "Scoring hidden Markov models." Bioinformatics 13, no. 2 (1997): 191–99. http://dx.doi.org/10.1093/bioinformatics/13.2.191.

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Eddy, S. R. "Profile hidden Markov models." Bioinformatics 14, no. 9 (October 1, 1998): 755–63. http://dx.doi.org/10.1093/bioinformatics/14.9.755.

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Dannemann, Jörn. "Semiparametric Hidden Markov Models." Journal of Computational and Graphical Statistics 21, no. 3 (July 2012): 677–92. http://dx.doi.org/10.1080/10618600.2012.681264.

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Farcomeni, Alessio. "Hidden Markov partition models." Statistics & Probability Letters 81, no. 12 (December 2011): 1766–70. http://dx.doi.org/10.1016/j.spl.2011.07.012.

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Dissertations / Theses on the topic "Hidden Markov Models"

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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.
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.
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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 to the task. After that, a hill-climbing formula is developed and applied. These first three methods reveal a number of interesting and useful insights into the problem. However, it is the fourth method which produces an algorithm that is then used for direct comparison vAth the Baum-Welch algorithm: examples of global optima are collected, examined for common features and patterns, and then a rule is induced. The resulting rule is implemented in *C' and tested against a battery of Baum-Welch based programs. In the limited range of tests carried out to date, the models produced by the new algorithm yield optima which have not been surpassed by (and are typically much better than) the Baum-Welch models. However, far more analysis and testing is required and in its current form the algorithm is not fast enough for realistic application.
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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 augmentation approach, we derive two forms of the likelihood function of a HMM in closed form, namely the observed and the conditional likelihoods. Subsequently, we develop several modified versions of the Akaike information criterion (AIC) and Bayesian information criterion (BIC) approximated under the Bayesian principle. We also develop several versions for the deviance information criterion (DIC). These proposed versions are based on the type of likelihood, i.e. conditional or observed likelihood, and also on whether the hidden states are dealt with as missing data or additional parameters in the model. This latter point is referred to as the concept of focus. Finally, we consider model selection from a predictive viewpoint. To this end, we develop the so-called widely applicable information criterion (WAIC). We assess the performance of these various proposed criteria via simulation studies and real-data applications. In this thesis, we apply Poisson HMMs to model the spatial dependence analysis in count data via an application to traffic safety crashes for three highways in the UK. The ultimate interest is in identifying highway segments which have distinctly higher crash rates. Selecting an optimal number of states is an important part of the interpretation. For this purpose, we employ model selection criteria to determine the optimal number of states. We also use several goodness-of-fit checks to assess the model fitted to the data. We implement an MCMC algorithm and check its convergence. We examine the sensitivity of the results to the prior specification, a potential problem given small sample sizes. The Poisson HMMs adopted can provide a different model for analysing spatial dependence on networks. It is possible to identify segments with a higher posterior probability of classification in a high risk state, a task that could prioritise management action.
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Van, Gael Jurgen. "Bayesian nonparametric hidden Markov models." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610196.

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Lystig, Theodore C. "Evaluation of hidden Markov models /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/9597.

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Santos, Leonor Marques Pompeu dos. "Hidden Markov models for credit risk." Master's thesis, Instituto Superior de Economia e Gestão, 2015. http://hdl.handle.net/10400.5/11061.

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Mestrado em Matemática Financeira
A análise do Risco de Crédito, a avaliação do risco de defafult ou de redução do valor de mercado causado por alterações na qualidade de crédito, tem sido um tema vastamente estudado ao longo dos últimos trinta anos e é hoje mais relevante que nunca, com o mundo ainda a recuperar das consequências de uma crise financeira, na sua génese induzida por uma observação imperfeita deste tipo de risco. Tal como alguns dos modelos apresentados anteriormente, o modelo apresentado nesta dissertação assume que os eventos de default estão directamente ligados a uma variável associada ao risco, partindo de um modelo simples que assume que o default segue um Modelo Oculto de Markov Binomial de dois estados, ou seja, um modelo que considera apenas dois "estados de risco" possíveis para explicar na totalidade a ocorrência de default, e aproximando-o a um Modelo Oculto de Markov Poisson, com todas as simplificações computacionais associadas a esta aproximação, tentando, ao mesmo tempo, traduzir o modelo para um cenário menos extremo, com a inclusão de um nível de risco intermédio.
Credit Risk measurement, the evaluation of the risk of default or reduction in market value caused by changes in credit quality, has been a broadly studied subject over the last thirty years and is now more relevant than ever, when the world is still suffering the consequences of the break of a financial crisis in its genesis induced by a false observation of this kind of risk. Just like some of the previous studies, the model presented in this dissertation assumes that default events are directly connected to risk state variables, starting from a very simple model that assumes defaults to follow a two-state Binomial Hidden Markov Model, considering only two different risk categories to fully explain default occurrence, and approximating it to a Poisson Hidden Markov Model, with all the computational simplifications brought by this approximation, trying, at the same time, to translate the model into a less extreme framework, with the addition of an intermediate risk level, a "normal" risk state.
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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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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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Foreman, Lindsay Anne. "Bayesian computation for hidden Markov models." Thesis, Imperial College London, 1994. http://hdl.handle.net/10044/1/11490.

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Staples, Jonathan Peter. "Hidden Markov models for credit risk." Thesis, Imperial College London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440498.

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Books on the topic "Hidden Markov Models"

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Westhead, David R., and M. S. Vijayabaskar, eds. Hidden Markov Models. New York, NY: 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. Cham: 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. Dordrecht: 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. Boston, MA: 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. Boston, MA: 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. New York, NY: Springer New York, 2005. http://dx.doi.org/10.1007/0-387-28982-8.

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Hidden Markov models for bioinformatics. Dordrecht: Kluwer Academic Publishers, 2001.

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Cappe, Olivier. Inference in hidden Markov models. New York, NY: Springer, 2005.

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

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

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Book chapters on the topic "Hidden Markov Models"

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Forsyth, David. "Hidden Markov Models." In Applied Machine Learning, 305–32. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18114-7_13.

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Fink, Gernot A. "Hidden Markov Models." In Markov Models for Pattern Recognition, 71–106. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6308-4_5.

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Murty, M. Narasimha, and V. Susheela Devi. "Hidden Markov Models." In Undergraduate Topics in Computer Science, 103–22. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-495-1_5.

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Sucar, Luis Enrique. "Hidden Markov Models." In Probabilistic Graphical Models, 63–82. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6699-3_5.

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Fieguth, Paul. "Hidden Markov Models." In Statistical Image Processing and Multidimensional Modeling, 215–39. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7294-1_7.

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Ewens, Warren J., and Gregory R. Grant. "Hidden Markov Models." In Statistical Methods in Bioinformatics, 327–47. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3247-4_11.

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Govea, Alejandro Dizan Vasquez. "Hidden Markov Models." In Springer Tracts in Advanced Robotics, 45–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13642-9_4.

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El-Beze, Marc, and Bernard Merialdo. "Hidden Markov Models." In Text, Speech and Language Technology, 263–84. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-015-9273-4_16.

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Vaseghi, Saeed V. "Hidden Markov Models." In Advanced Signal Processing and Digital Noise Reduction, 111–39. Wiesbaden: Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-322-92773-6_4.

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Bosch, Antal van den, Bernhard Hengst, John Lloyd, Risto Miikkulainen, Hendrik Blockeel, and Hendrik Blockeel. "Hidden Markov Models." In Encyclopedia of Machine Learning, 493–95. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_362.

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Conference papers on the topic "Hidden Markov Models"

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Radenen, Mathieu, and Thierry Artieres. "Contextual Hidden Markov Models." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288328.

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"15 - Hidden Markov Models." In 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628684.

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Gales, M. J. F., and Steve J. Young. "Segmental hidden Markov models." In 3rd European Conference on Speech Communication and Technology (Eurospeech 1993). ISCA: ISCA, 1993. http://dx.doi.org/10.21437/eurospeech.1993-354.

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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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Ya-Ti Peng, Ching-Yung Lin, Ming-Ting Sun, and Kun-Cheng Tsai. "Healthcare audio event classification using Hidden Markov Models and Hierarchical Hidden Markov Models." In 2009 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2009. http://dx.doi.org/10.1109/icme.2009.5202720.

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Smith, S. F. "Truncated Profile Hidden Markov Models." In 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. IEEE, 2005. http://dx.doi.org/10.1109/cibcb.2005.1594926.

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Rosti, A.-V. I., and M. J. F. Gales. "Factor analysed hidden Markov models." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5743950.

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Rosti and Gales. "Factor analysed hidden Markov models." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1005898.

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Kimber, D., and M. Bush. "Situated state hidden Markov models." In Proceedings of ICASSP '93. IEEE, 1993. http://dx.doi.org/10.1109/icassp.1993.319350.

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Mohd Yusoff, Mohd Izhan, Ibrahim Mohamed, and Mohd Rizam Abu Bakar. "Hidden Markov models: An insight." In 2014 International Conference on Information Technology and Multimedia (ICIMU). IEEE, 2014. http://dx.doi.org/10.1109/icimu.2014.7066641.

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Reports on the topic "Hidden Markov Models"

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Ghahramani, Zoubin, and Michael I. Jordan. Factorial Hidden Markov Models. Fort Belvoir, VA: Defense Technical Information Center, January 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. Fort Belvoir, VA: Defense Technical Information Center, March 2001. http://dx.doi.org/10.21236/ada415930.

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Thrun, Sebastian, and John Langford. Monte Carlo Hidden Markov Models. Fort Belvoir, VA: Defense Technical Information Center, December 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), February 2021. http://dx.doi.org/10.2172/1766975.

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Del Rose, Michael S., Philip Frederick, and Christian Wagner. Using Evidence Feed-Forward Hidden Markov Models. Fort Belvoir, VA: Defense Technical Information Center, May 2010. http://dx.doi.org/10.21236/ada543331.

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

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Dey, Subhrakanti, and Steven I. Marcus. A Framework for Mixed Estimation of Hidden Markov Models. Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada438575.

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Gader, Paul. Hidden Markov Models for Sensor Fusion of EMI and GRP. Fort Belvoir, VA: Defense Technical Information Center, November 2003. http://dx.doi.org/10.21236/ada422405.

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Carin, Lawrence. Matching Pursuits & Hidden Markov Models for Processing IR Imagery. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada384419.

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Lewis, Arthur. Seasonal Hidden Markov Models for Stochastic Time Series with Periodically Varying Characteristics. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6932.

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