Literatura académica sobre el tema "Hidden semi-Markov chains"
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Artículos de revistas sobre el tema "Hidden semi-Markov chains"
Guédon, Yann. "Hidden hybrid Markov/semi-Markov chains". Computational Statistics & Data Analysis 49, n.º 3 (junio de 2005): 663–88. http://dx.doi.org/10.1016/j.csda.2004.05.033.
Texto completoElliott, Robert, Nikolaos Limnios y Anatoliy Swishchuk. "Filtering hidden semi-Markov chains". Statistics & Probability Letters 83, n.º 9 (septiembre de 2013): 2007–14. http://dx.doi.org/10.1016/j.spl.2013.05.007.
Texto completoGuédon, Yann. "Estimating Hidden Semi-Markov Chains From Discrete Sequences". Journal of Computational and Graphical Statistics 12, n.º 3 (septiembre de 2003): 604–39. http://dx.doi.org/10.1198/1061860032030.
Texto completoGuédon, Yann. "Computational methods for discrete hidden semi-Markov chains". Applied Stochastic Models in Business and Industry 15, n.º 3 (julio de 1999): 195–224. http://dx.doi.org/10.1002/(sici)1526-4025(199907/09)15:3<195::aid-asmb376>3.0.co;2-f.
Texto completoLapuyade-Lahorgue, Jérôme y Wojciech Pieczynski. "Unsupervised segmentation of hidden semi-Markov non-stationary chains". Signal Processing 92, n.º 1 (enero de 2012): 29–42. http://dx.doi.org/10.1016/j.sigpro.2011.06.001.
Texto completoGuédon, Yann. "Exploring the state sequence space for hidden Markov and semi-Markov chains". Computational Statistics & Data Analysis 51, n.º 5 (febrero de 2007): 2379–409. http://dx.doi.org/10.1016/j.csda.2006.03.015.
Texto completoCrowder, Martin. "Semi-Markov Chains and Hidden Semi-Markov Models Toward Applications by Vlad Stefan Barbu, Nikolaos Limnios". International Statistical Review 77, n.º 2 (agosto de 2009): 307. http://dx.doi.org/10.1111/j.1751-5823.2009.00085_8.x.
Texto completoVotsi, I. y N. Limnios. "Estimation of the intensity of the hitting time for semi-Markov chains and hidden Markov renewal chains". Journal of Nonparametric Statistics 27, n.º 2 (6 de febrero de 2015): 149–66. http://dx.doi.org/10.1080/10485252.2015.1009369.
Texto completoLapuyade-Lahorgue, Jérôme y Wojciech Pieczynski. "Unsupervised segmentation of new semi-Markov chains hidden with long dependence noise". Signal Processing 90, n.º 11 (noviembre de 2010): 2899–910. http://dx.doi.org/10.1016/j.sigpro.2010.04.008.
Texto completoNegrón, C., M. L. Contador, B. D. Lampinen, S. G. Metcalf, Y. Guédon, E. Costes y T. M. DeJong. "USING HIDDEN SEMI-MARKOV CHAINS TO COMPARE THE SHOOT STRUCTURE OF THREE DIFFERENT ALMOND CULTIVARS". Acta Horticulturae, n.º 1068 (febrero de 2015): 67–75. http://dx.doi.org/10.17660/actahortic.2015.1068.7.
Texto completoTesis sobre el tema "Hidden semi-Markov chains"
Akbar, Ihsan Ali. "Statistical Analysis of Wireless Systems Using Markov Models". Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/26089.
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Fernandes, Clément. "Chaînes de Markov triplets et segmentation non supervisée d'images". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS019.
Texto completoHidden Markov chains (HMC) are widely used in unsupervised Bayesian hidden discrete data restoration. They are very robust and, in spite of their simplicity, they are sufficiently efficient in many situations. In particular for image segmentation, despite their mono-dimensional nature, they are able, through a transformation of the bi-dimensional images into mono-dimensional sequences with Peano scan (PS), to give satisfying results. However, sometimes, more complex models such as hidden Markov fields (HMF) may be preferred in spite of their increased time complexity, for their better results. Moreover, hidden Markov models (the chains as well as the fields) have been extended to pairwise and triplet Markov models, which can be of interest in more complex situations. For example, when sojourn time in hidden states is not geometrical, hidden semi-Markov (HSMC) chains tend to perform better than HMC, and such is also the case for hidden evidential Markov chains (HEMC) when data are non-stationary. In this thesis, we first propose a new triplet Markov chain (TMC), which simultaneously extends HSMC and HEMC. Based on hidden triplet Markov chains (HTMC), the new hidden evidential semi-Markov chain (HESMC) model can be used in unsupervised framework, parameters being estimated with Expectation-Maximization (EM) algorithm. We validate its interest through some experiments on synthetic data. Then we address the problem of mono-dimensionality of the HMC with PS model in image segmentation by introducing the “contextual” Peano scan (CPS). It consists in associating to each index in the HMC obtained from PS, two observations on pixels which are neighbors of the pixel considered in the image, but are not its neighbors in the HMC. This gives three observations on each point of the Peano scan, which leads to a new conditional Markov chain (CMC) with a more complex structure, but whose posterior law is still Markovian. Therefore, we can apply the usual parameter estimation method: Stochastic Expectation-Maximization (SEM), as well as study unsupervised segmentation Marginal Posterior Mode (MPM) so obtained. The CMC with CPS based supervised and unsupervised MPM are compared to the classic scan based HMC-PS and the HMF through experiments on artificial images. They improve notably the former, and can even compete with the latter. Finally, we extend the CMC-CPS to Pairwise Conditional Markov (CPMC) chains and two particular triplet conditional Markov chain: evidential conditional Markov chains (CEMC) and conditional semi-Markov chains (CSMC). For each of these extensions, we show through experiments on artificial images that these models can improve notably their non conditional counterpart, as well as the CMC with CPS, and can even compete with the HMF. Beside they allow the generality of markovian triplets to better play its part in image segmentation, while avoiding the substantial time complexity of triplet Markov fields
Li, Haoyu. "Recent hidden Markov models for lower limb locomotion activity detection and recognition using IMU sensors". Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEC041.
Texto completoThe thesis context is that of the quantified self, a movement born in California that consists in getting to know oneself better by measuring data relating to one’s body and activities. The research work consisted in developing algorithms for analyzing signals from an IMU (Inertial Measurement Unit) sensor placed on the leg to recognize different movement activities such as walking, running, stair climbing... These activities are recognizable by the shape of the sensor’s acceleration and angular velocity signals, both tri-axial, during leg movement and gait cycle.To address the recognition problem, the thesis work resulted in the construction of a particular hidden Markov chain, called semi-triplet Markov chain, which combines a semi-Markov model and a Gaussian mixture model in a triplet Markov model. This model is both adapted to the nature of the gait cycle, and to the sequence of activities as it can be carried out in daily life. To adapt the model parameters to the differences in human morphology and behavior, we have developed algorithms for estimating parameters both off-line and on-line.To establish the classification and learning performance of the algorithms, we conducted experiments on the basis of recordings collected during the thesis and on public dataset. The results are systematically compared with state-of-the-art algorithms
Votsi, Irène. "Evaluation des risques sismiques par des modèles markoviens cachés et semi-markoviens cachés et de l'estimation de la statistique". Thesis, Compiègne, 2013. http://www.theses.fr/2013COMP2058.
Texto completoThe first chapter describes the definition of the subject under study, the current state of science in this area and the objectives. In the second chapter, continuous-time semi-Markov models are studied and applied in order to contribute to seismic hazard assessment in Northern Aegean Sea (Greece). Expressions for different important indicators of the semi- Markov process are obtained, providing forecasting results about the time, the space and the magnitude of the ensuing strong earthquake. Chapters 3 and 4 describe a first attempt to model earthquake occurrence by means of discrete-time hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs), respectively. A nonparametric estimation method is followed by means of which, insights into features of the earthquake process are provided which are hard to detect otherwise. Important indicators concerning the levels of the stress field are estimated by means of the suggested HMM and HSMM. Chapter 5 includes our main contribution to the theory of stochastic processes, the investigation and the estimation of the discrete-time intensity of the hitting time (DTIHT) for the first time referring to semi-Markov chains (SMCs) and hidden Markov renewal chains (HMRCs). A simple formula is presented for the evaluation of the DTIHT along with its statistical estimator for both SMCs and HMRCs. In addition, the asymptotic properties of the estimators are proved, including strong consistency and asymptotic normality. In chapter 6, a comparison between HMMs and HSMMs in a Markov and a semi-Markov framework is given in order to highlight possible differences in their stochastic behavior partially governed by their transition probability matrices. Basic results are presented in the general case where specific distributions are assumed for sojourn times as well as in the special case concerning the models applied in the previous chapters, where the sojourn time distributions are estimated non-parametrically. The impact of the differences is observed through the calculation of the mean value and the variance of the number of steps that the Markov chain (HMM case) and the EMC (HSMM case) need to make for visiting for the first time a particular state. Finally, Chapter 7 presents concluding remarks, perspectives and future work
Keller, Oliver. "Probabilistic Methods for Computational Annotation of Genomic Sequences". Doctoral thesis, 2011. http://hdl.handle.net/11858/00-1735-0000-0006-B6A7-D.
Texto completoLangrock, Roland. "On some special-purpose hidden Markov models". Doctoral thesis, 2011. http://hdl.handle.net/11858/00-1735-0000-0006-B6AF-E.
Texto completoLibros sobre el tema "Hidden semi-Markov chains"
N, Limnios, ed. Semi-Markov chains and hidden semi-Markov models toward applications: Their use in reliability and DNA analysis. New York: Springer, 2008.
Buscar texto completoSemi-Markov Chains and Hidden Semi-Markov Models toward Applications. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-73173-5.
Texto completoLimnios, Nikolaos y Vlad Stefan Barbu. Semi-Markov Chains and Hidden Semi-Markov Models Toward Applications: Their Use in Reliability and DNA Analysis. Springer, 2009.
Buscar texto completoCapítulos de libros sobre el tema "Hidden semi-Markov chains"
Derouault, Anne-Marie y Bernard Merialdo. "Language modelling using a hidden Markov chain with application to automatic transcription of French stenotypy". En Semi-Markov Models, 475–85. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-0574-1_29.
Texto completoVidyasagar, M. "Introduction to Large Deviation Theory". En Hidden Markov Processes. Princeton University Press, 2014. http://dx.doi.org/10.23943/princeton/9780691133157.003.0005.
Texto completoActas de conferencias sobre el tema "Hidden semi-Markov chains"
Barbu, Vlad-Stefan. "Reliability modeling with hidden Markov and semi-Markov chains". En 2013 IEEE Integration of Stochastic Energy in Power Systems Workshop (ISEPS). IEEE, 2013. http://dx.doi.org/10.1109/iseps.2013.6707952.
Texto completoLapuyade-Lahorgue, Jérôme y Wojciech Pieczynski. "Unsupervised Segmentation of Hidden Semi-Markov Non Stationary Chains". En Bayesian Inference and Maximum Entropy Methods In Science and Engineering. AIP, 2006. http://dx.doi.org/10.1063/1.2423293.
Texto completoPieczynski, W. "Modeling non stationary hidden semi-markov chains with triplet markov chains and theory of evidence". En 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628689.
Texto completoLapuyade-Lahorgue, Jérôme y Wojciech Pieczynski. "Partially Markov models and unsupervised segmentation of semi-Markov chains hidden with long dependence noise". En Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0029.
Texto completoPetetin, Yohan y Francois Desbouvries. "A semi-exact sequential Monte Carlo filtering algorithm in Hidden Markov Chains". En 2012 11th International Conference on Information Sciences, Signal Processing and their Applications (ISSPA). IEEE, 2012. http://dx.doi.org/10.1109/isspa.2012.6310621.
Texto completoXia, Ning, Aishuang Li, Guizhi Zhu, Xiaoguo Niu, Chunsheng Hou y Yangying Gan. "Study of Branching Responses of One Year Old Branches of Apple Trees to Heading Using Hidden Semi-Markov Chains". En 2009 Third International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA). IEEE, 2009. http://dx.doi.org/10.1109/pma.2009.10.
Texto completoZhou, Fan, Qiang Gao, Goce Trajcevski, Kunpeng Zhang, Ting Zhong y Fengli Zhang. "Trajectory-User Linking via Variational AutoEncoder". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/446.
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