Academic literature on the topic 'Structures de données probabilistes'
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Journal articles on the topic "Structures de données probabilistes":
Areni, Charles S. "Modèle propositionnel probabiliste de la structure de l'argument et de l'acceptation du message." Recherche et Applications en Marketing (French Edition) 18, no. 1 (March 2003): 95–121. http://dx.doi.org/10.1177/076737010301800105.
Florescu, Liviu. "Structures syntopogènes probabilistes." Publicationes Mathematicae Debrecen 28, no. 1-2 (July 1, 2022): 15–24. http://dx.doi.org/10.5486/pmd.1981.28.1-2.03.
BOUDON, Raymond. "Les statistiques peuvent-elles donner une image réelle de la réalité sociale?" Sociologie et sociétés 8, no. 2 (September 30, 2002): 141–56. http://dx.doi.org/10.7202/001080ar.
Li, Haizhou, François Pinet, and Farouk Toumani. "Test de simulation pour les processus métiers centrés données probabilistes." Ingénierie des systèmes d'information 19, no. 4 (August 28, 2014): 35–60. http://dx.doi.org/10.3166/isi.19.4.35-60.
Maranda, Pierre. "Cartographie sémantique : esquisse sémiographique de la Québécoise." Cahiers de géographie du Québec 25, no. 64 (April 12, 2005): 71–86. http://dx.doi.org/10.7202/021506ar.
Stepanov, Aleksandr. "Le recours à l’évidence dans l’élaboration des normes internes." Civitas Europa N° 51, no. 2 (June 14, 2024): 49–59. http://dx.doi.org/10.3917/civit.051.0049.
Gnacadja, Claude, Armel Mouketou, Ephrem Nzangue, Chamfort Biroungou, and Jacques François Mavoungou. "Analyse de Quelques Caractéristiques de la Filière Maraichage dans Trois Provinces du Gabon." European Scientific Journal, ESJ 18, no. 27 (August 31, 2022): 296. http://dx.doi.org/10.19044/esj.2022.v18n27p296.
Hantrais, Linda, and Marie-Thérèse Letablier. "Données démographiques et structures familiales." Informations sociales 124, no. 4 (2005): 16. http://dx.doi.org/10.3917/inso.124.0016.
Zhang, Yi, and Stéphane Commend. "Calculs probabilistes des déplacements dus à la réalisation de tunnels à l’aide d’un modèle aux éléments finis." Revue Française de Géotechnique, no. 167 (2021): 5. http://dx.doi.org/10.1051/geotech/2021018.
Clément Maria. "Algorithmes et structures de données en topologie algorithmique." Bulletin 1024, no. 8 (April 2016): 125–27. http://dx.doi.org/10.48556/sif.1024.8.125.
Dissertations / Theses on the topic "Structures de données probabilistes":
Perrin, Frédéric. "Prise en compte des données expérimentales dans les modèles probabilistes pour la prévision de la durée de vie des structures." Clermont-Ferrand 2, 2008. http://www.theses.fr/2008CLF21823.
El, Abri Marwa. "Probabilistic relational models learning from graph databases." Thesis, Nantes, 2018. http://www.theses.fr/2018NANT4019/document.
Historically, Probabilistic Graphical Models (PGMs) are a solution for learning from uncertain and flat data, also called propositional data or attributevalue representations. In the early 2000s, great interest was addressed to the processing of relational data which includes a large number of objects participating in different relations. Probabilistic Relational Models (PRMs) present an extension of PGMs to the relational context. With the rise of the internet, numerous technological innovations and web applications are driving the dramatic increase of various and complex data. Consequently, Big Data has emerged. Several types of data stores have been created to manage this new data, including the graph databases. Recently there has been an increasing interest in graph databases to model objects and interactions. However, all PRMs structure learning use wellstructured data that are stored in relational databases. Graph databases are unstructured and schema-free data stores. Edges between nodes can have various signatures. Since, relationships that do not correspond to an ER model could be depicted in the database instance. These relationships are considered as exceptions. In this thesis, we are interested by this type of data stores. Also, we study two kinds of PRMs namely, Direct Acyclic Probabilistic Entity Relationship (DAPER) and Markov Logic Networks (MLNs). We propose two significant contributions. First, an approach to learn DAPERs from partially structured graph databases. A second approach consists to benefit from first-order logic to learn DAPERs using MLN framework to take into account the exceptions that are dropped during DAPER learning. We are conducting experimental studies to compare our proposed methods with existing approaches
Fekete, Eric. "Etude probabiliste d'arbres issus de l'algorithmique." Versailles-St Quentin en Yvelines, 2007. http://www.theses.fr/2007VERS0016.
The aim of this thesis is the study of the behavior of trees used in analysis of algorithms. We use probabilistic techniques to study various random objects connected with trees. We formally define the trees we deal with and introduce our main results in chapter one. Each of the three other parts of the thesis contains a specific random phenomenon. We first establish a result on the asymptotics of the rescaled occupation measure of a branching random walk on binary search trees (BSTs). Under weak hypothesis on the increments, we show that this measure converges to a deterministic measure depending on the stable law whose domain of attraction contains the law of the increments. The proof is based on some fundamental properties of the structure of BST. One of them is the result by Louchard on the height of a typical node. This convergence allows to obtain results on two other objects associated to the BST : homogeneous fragmentations of ]0, 1[ and recursive trees. The second study is also on BSTs. We study the profile of the tree (number of leaves at each level) specifying the types of the leaves : arms are the leaves whose brother is an internal node and feet are the leaves whose brother is also a leaf. We use a vector whose coordinates are the level polynomials of arms and feet. The coefficient of order k of these polynomials is the number of arms and feet at level k in the BST of size n. Comparing the two projections of this vector on the eigenspaces of a so-called evolution matrix, we obtain an almost sure and a L2-convergence of a martingale vector, connected to the profile, to a vector associated to the limit of the Jabbour martingale. Finally, the last part deals with another kind of random trees : the suffix trees. These trees are defined from an infinite word and its randomness is given by the source that creates the word. Here we are concerned with -mixing sources. We prove that the fill-up level of a suffix tree with n keys, normalized by log n, converges almost surely to a constant depending on the source. By definition of the suffix trees, the study of this parameter happens to be a word apparition time issue. We obtain the convergence using results of Abadi and Vergne in this field
Scholler, Rémy. "Analyse de données de signalisation mobile pour l’étude de la mobilité respectueuse de la vie privée : Application au secteur du transport routier de marchandises." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCD001.
Mobile network operators have a significant data source derived from communications of all connected objects (not just smartphones) with the network. These signaling data is a massive source of location data and are regularly used for the mobility analysis. However, potential uses face two major challenges: their low spatiotemporal precision and their highly sensitive nature concerning privacy.In the first phase, the thesis work enhances the understanding of the mobility state (stationary or in motion), speed, direction of movement of connected objects, and the route they take on a transportation infrastructure (e.g., road or rail).In the second phase, we demonstrate how to ensure the confidentiality of continuously produced mobility statistics. The use of signaling data, whether related to users or various connected objects, is legally regulated. For the study of mobility, operators tend to publish anonymized statistics (aggregated data). Specifically, the aim is to calculate complex and anonymized mobility statistics "on the fly" using differential privacy methods and probabilistic data structures (such as Bloom filters).Finally, in the third phase, we illustrate the potential of signaling data and the proposed approaches in this manuscript for quasi-real-time calculation of anonymous statistics on road freight transport. However, this is just an example of what could apply to other subjects analyzing population behaviors and activities with significant public and economic policy implications
Boyer, Laurent. "Apprentissage probabiliste de similarités d'édition." Phd thesis, Université Jean Monnet - Saint-Etienne, 2011. http://tel.archives-ouvertes.fr/tel-00718835.
Jabbour-Hattab, Jean. "Une approche probabiliste du profil des arbres binaires de recherche." Versailles-St Quentin en Yvelines, 2001. http://www.theses.fr/2001VERS002V.
Barriot, Roland. "Intégration des connaissances biologiques à l'échelle de la cellule." Bordeaux 1, 2005. http://www.theses.fr/2005BOR13100.
Mohamed, Hanène. "Etude probabiliste d'algorithmes en arbre." Paris 6, 2007. https://tel.archives-ouvertes.fr/tel-00270742.
In this thesis a general class of tree algorithms is analyzed. It is shown that, by using an appropriate probabilistic representation of the quantities of interest, the asymptotic behavior of these algorithms can be obtained quite easily. This approach gives a unified probabilistic treatment of these questions. It simplifies and extends some of the results known in this domain
Mohamed, Hanene. "Étude Probabiliste d'Algorithmes en Arbre." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2007. http://tel.archives-ouvertes.fr/tel-00270742.
Reype, Christophe. "Modélisation probabiliste et inférence bayésienne pour l’analyse de la dynamique des mélanges de fluides géologiques : détection des structures et estimation des paramètres." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0235.
The analysis of hydrogeochemical data aims to improve the understanding of mass transfer in the sub-surface and the Earth’s crust. This work focuses on the study of fluid-fluid interactions through fluid mixing systems, and more particularly on the detection of the compositions of the mixing sources. The detection is done by means of a point process: the proposed model is unsupervised and applicable to multidimensional data. Physical knowledge of the mixtures and geological knowledge of the data are directly integrated into the probability density of a Gibbs point process, which distributes point patterns in the data space, called the HUG model. The detected sources form the point pattern that maximises the probability density of the HUG model. This probability density is known up to the normalization constant. The knowledge related to the parameters of the model, either acquired experimentally or by using inference methods, is integrated in the method under the form of prior distributions. The configuration of the sources is obtained by a simulated annealing algorithm and Markov Chain Monte Carlo (MCMC) methods. The parameters of the model are estimated by an approximate Bayesian computation method (ABC). First, the model is applied to synthetic data, and then to real data. The parameters of the model are then estimated for a synthetic data set with known sources. Finally, the sensitivity of the model to data uncertainties, to parameters choices and to algorithms set-up is studied
Books on the topic "Structures de données probabilistes":
Aho, Alfred V. Structures de données et algorithmes. Paris: InterÉditions, 1987.
Wirth, Niklaus. Algorithmes et structures de données. 2nd ed. Paris: Eyrolles, 1989.
Guyomard, Marc. Structures de données et méthodes formelles. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0200-8.
Carrez, Christian. Des structures aux bases de données. Paris: Dunod, 1990.
Guyomard, Marc. Structures de données et méthodes formelles. Paris: Springer Paris, 2011.
Boussard, Jean-Claude. Programmation avancée: Algorithmique et structures de données. Paris: Eyrolles, 1987.
Lipschutz, Seymour. Les structures de données: Cours et problèmes. Auckland: McGraw-Hill, 1987.
Pichat, Étienne. Ingénierie des données: Systèmes d'information, modèles et bases de données. Paris: Masson, 1990.
Gabrini, Philippe J. ADA 95: Orientation objet, structures de données et algorithmes. Bruxelles: De Boeck Université, 2000.
Günther, Oliver. Efficient structures for geometric data management. Berlin: Springer-Verlag, 1988.
Book chapters on the topic "Structures de données probabilistes":
Bretto, Alain, Alain Faisant, and François Hennecart. "(Di)graphes et structures de données." In Éléments de théorie des graphes, 61–98. Paris: Springer Paris, 2012. http://dx.doi.org/10.1007/978-2-8178-0281-7_3.
Guyomard, Marc. "étude de quelques structures outils." In Structures de données et méthodes formelles, 77–105. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0200-8_3.
Guyomard, Marc. "Mathématiques pour la spécification et les structures de données." In Structures de données et méthodes formelles, 15–57. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0200-8_1.
Guyomard, Marc. "Tableaux flexibles." In Structures de données et méthodes formelles, 377–99. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0200-8_10.
Guyomard, Marc. "Spécifications + Fonction d’abstraction + Calcul = Programme." In Structures de données et méthodes formelles, 59–76. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0200-8_2.
Guyomard, Marc. "Analyse d’algorithmes." In Structures de données et méthodes formelles, 107–28. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0200-8_4.
Guyomard, Marc. "Exemples." In Structures de données et méthodes formelles, 129–44. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0200-8_5.
Guyomard, Marc. "Ensembles de clés scalaires." In Structures de données et méthodes formelles, 147–271. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0200-8_6.
Guyomard, Marc. "Ensembles de clés structurées." In Structures de données et méthodes formelles, 273–311. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0200-8_7.
Guyomard, Marc. "Files simples." In Structures de données et méthodes formelles, 313–25. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0200-8_8.
Reports on the topic "Structures de données probabilistes":
Brinkerhoff, Derick W., Sarah Frazer, and Lisa McGregor. S'adapter pour apprendre et apprendre pour s'adapter : conseils pratiques tirés de projets de développement internationaux. RTI Press, January 2018. http://dx.doi.org/10.3768/rtipress.2018.pb.0015.1801.fr.
Snyder, D. B., A. Vaillancourt, B. A. Kjarsgaard, G. Savard, and E. A. de Kemp. 3-D mantle structure of the Superior Craton. Natural Resources Canada/CMSS/Information Management, 2024. http://dx.doi.org/10.4095/p8zz9che61.