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Artykuły w czasopismach na temat "Chaînes de semi-Markov cachées"
Dridi, Noura, Yves Delignon i Wadih Sawaya. "Critères BIC et AIC pour les chaînes de Markov cachées. Application aux communications numériques". Traitement du signal 31, nr 3-4 (28.10.2014): 383–400. http://dx.doi.org/10.3166/ts.31.383-400.
Pełny tekst źródłaBakry, Dominique, Xavier Milhaud i Pierre Vandekerkhove. "Statistique de chaînes de Markov cachées à espace d'états fini. Le cas non stationnaire". Comptes Rendus de l'Académie des Sciences - Series I - Mathematics 325, nr 2 (lipiec 1997): 203–6. http://dx.doi.org/10.1016/s0764-4442(97)84600-9.
Pełny tekst źródłaBickel, P. "La vraisemblance des chaînes de Markov cachées se comporte comme celle des variables i.i.d." Annales de l'Institut Henri Poincare (B) Probability and Statistics 38, nr 6 (grudzień 2002): 825–46. http://dx.doi.org/10.1016/s0246-0203(02)01134-2.
Pełny tekst źródłaRozprawy doktorskie na temat "Chaînes de semi-Markov cachées"
Lacour, Claire. "Estimation non paramétrique adaptative pour les chaînes de Markov et les chaînes de Markov cachées". Phd thesis, Université René Descartes - Paris V, 2007. http://tel.archives-ouvertes.fr/tel-00180107.
Pełny tekst źródłaBarbu, Vlad. "Estimation des chaînes semi-markoviennes et des chaînes semi-markoviennes cachées en vue d'applications en fiabilité et en biologie". Compiègne, 2005. http://www.theses.fr/2005COMP1568.
Pełny tekst źródłaThe first part of my thesis concerns the discrete time semi-Markov models and the associated nonparametric estimation. The obtained results are used for deriving estimators of the systems reliability and of the associated measures. The asymptotic properties of the estimators are studied. An example illustrates how to practically compute the reliability indicators. The second part of my thesis is devoted to the estimation of hidden semi-Markov models. The asymptotic properties of the estimators are studied and an EM algorithm is proposed. An application in genetics for detecting the CpG islands in a DNA sequence shows the interest of our researches
RAFI, Selwa. "Chaînes de Markov cachées et séparation non supervisée de sources". Phd thesis, Institut National des Télécommunications, 2012. http://tel.archives-ouvertes.fr/tel-00995414.
Pełny tekst źródłaRafi, Selwa. "Chaînes de Markov cachées et séparation non supervisée de sources". Thesis, Evry, Institut national des télécommunications, 2012. http://www.theses.fr/2012TELE0020/document.
Pełny tekst źródłaThe restoration problem is usually encountered in various domains and in particular in signal and image processing. It consists in retrieving original data from a set of observed ones. For multidimensional data, the problem can be solved using different approaches depending on the data structure, the transformation system and the noise. In this work, we have first tackled the problem in the case of discrete data and noisy model. In this context, the problem is similar to a segmentation problem. We have exploited Pairwise and Triplet Markov chain models, which generalize Hidden Markov chain models. The interest of these models consist in the possibility to generalize the computation procedure of the posterior probability, allowing one to perform bayesian segmentation. We have considered these methods for two-dimensional signals and we have applied the algorithms to retrieve of old hand-written document which have been scanned and are subject to show through effect. In the second part of this work, we have considered the restoration problem as a blind source separation problem. The well-known "Independent Component Analysis" (ICA) method requires the assumption that the sources be statistically independent. In practice, this condition is not always verified. Consequently, we have studied an extension of the ICA model in the case where the sources are not necessarily independent. We have introduced a latent process which controls the dependence and/or independence of the sources. The model that we propose combines a linear instantaneous mixing model similar to the one of ICA model and a probabilistic model on the sources with hidden variables. In this context, we show how the usual independence assumption can be weakened using the technique of Iterative Conditional Estimation to a conditional independence assumption
Rouan, Lauriane. "Apports des chaînes de Markov cachées à l'analyse de données de capture-recapture". Montpellier 2, 2007. http://www.theses.fr/2007MON20188.
Pełny tekst źródłaParis, Sébastien. "Extraction Automatique de Pistes Fréquentielles en Sonar Passif par Chaînes de Markov Cachées". Toulon, 2000. http://www.theses.fr/2000TOUL0013.
Pełny tekst źródłaThe frequency vs. Time image called lofargram in any passive sonar system is the key of the downstream information processing : the operator will investigate on this representation in order to classify the tar¬gets of interest and/or to track the targets that have generated them by the so-called Target Motion Analysis (TMA). Both need to use frequency lines. For TMA, constant but Doppler-shifted frequencies are necessary. Conversely, for classification, the fluctuations of frequency help the discrimination between targets. In this thesis, we are concerned by unstable frequency line tracker (FLT). The role of such a tracker is to estimate frequency lines. The fundamental problems of FLT come from the unknown num¬ber of lines, their dates of birth and death, their respective SNR's and the case of crossing lines. We propose a method based on Markov Chain modeling : each frequency line is assumed to follow a random walk; the state space is the lofargram frequency cell set vs. A discrete and finite set of slopes. The data are composed by each lofargram line. When one unique line is present, we propose a scaled version of algorithms encountered in Hidden Markov Models (HMM) literature : Viterbi algorithm (VA), Forward (F), Forward-Backward (FB). In case of several frequency lines, we derive a new scaled FB algorithm in which each probability is conditioned by the exclusive event : two lines cannot be simultaneously in the same spot. The algorithm works in two passes : first, the lines are extracted from the beginning to the end of the lofargram; then, we estimate the dates of birth and death of each of them. When those dates are equal, the line is discarded. Therefore, the number of lines must be a priori overestimated. Trials on synthetic but also real data have been conducted and allow us to conclude that this algorithm performs very correctly (in a operational sense)
Lapuyade-Lahorgue, Jérôme. "Sur diverses extensions des chaînes de Markov cachées avec application au traitement des signaux radar". Phd thesis, Institut National des Télécommunications, 2008. http://tel.archives-ouvertes.fr/tel-00473711.
Pełny tekst źródłaFernandes, 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.
Pełny tekst źródłaHidden 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
Ben, Mabrouk Mohamed. "Modèles de Markov triplets en restauration des signaux". Phd thesis, Institut National des Télécommunications, 2011. http://tel.archives-ouvertes.fr/tel-00694128.
Pełny tekst źródłaDridi, Noura. "Estimation aveugle de chaînes de Markov cachées simples et doubles : Application au décodage de codes graphiques". Thesis, Evry, Institut national des télécommunications, 2012. http://www.theses.fr/2012TELE0022.
Pełny tekst źródłaSince its birth, the technology of barcode is well investigated for automatic identification. When reading, a barcode can be degraded by a blur , caused by a bad focalisation and/ or a camera movement. The goal of this thesis is the optimisation of the receiver of 1D and 2D barcode from hidden and double Markov model and blind statistical estimation approaches. The first phase of our work consists of modelling the original image and the observed one using Hidden Markov model. Then, new algorithms for joint blur estimation and symbol detection are proposed, which take into account the non-stationarity of the hidden Markov process. Moreover, a method to select the most relevant model of the blur is proposed, based on model selection criterion. The method is also used to estimate the blur length. Finally, a new algorithm based on the double Markov chain is proposed to deal with digital communication through a long memory channel. Estimation of such channel is not possible using the classical detection algorithms based on the maximum likelihood due to the prohibitive complexity. New algorithm giving good trade off between complexity and performance is provided
Części książek na temat "Chaînes de semi-Markov cachées"
Chafaï, Djalil, i Florent Malrieu. "Chaînes de Markov cachées". W Recueil de Modèles Aléatoires, 93–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49768-5_7.
Pełny tekst źródłaCHEPTOU, Pierre-Olivier, Stéphane CORDEAU, Sebastian LE COZ i Nathalie PEYRARD. "Des HMM pour estimer la dynamique de la banque de graines chez les plantes". W Approches statistiques pour les variables cachées en écologie, 111–30. ISTE Group, 2022. http://dx.doi.org/10.51926/iste.9047.ch5.
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