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Статті в журналах з теми "Analyse probabiliste d'algorithmes"
Ben Bouallègue, Zied, Mariana Clare, and Matthieu Chevallier. "Prévisions météorologiques reposant sur l'intelligence artificielle : Une révolution peut en cacher une autre." La Météorologie, no. 126 (2024): 048. http://dx.doi.org/10.37053/lameteorologie-2024-0058.
Повний текст джерелаMétivier, M., and P. Priouret. "Théorèmes de convergence presque sure pour une classe d'algorithmes stochastiques à pas décroissant." Probability Theory and Related Fields 74, no. 3 (September 1987): 403–28. http://dx.doi.org/10.1007/bf00699098.
Повний текст джерелаДисертації з теми "Analyse probabiliste d'algorithmes"
Fontaine, Allyx. "Analyses et preuves formelles d'algorithmes distribués probabilistes." Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0091/document.
Повний текст джерелаProbabilistic algorithms are simple to formulate. However, theiranalysis can become very complex, especially in the field of distributedcomputing. We present algorithms - optimal in terms of bit complexityand solving the problems of MIS and maximal matching in rings - that followthe same scheme.We develop a method that unifies the bit complexitylower bound results to solve MIS, maximal matching and coloration problems.The complexity of these analyses, which can easily lead to errors,together with the existence of many models depending on implicit assumptionsmotivated us to formally model the probabilistic distributed algorithmscorresponding to our model (message passing, anonymous andsynchronous). Our aim is to formally prove the properties related to theiranalysis. For this purpose, we develop a library, called RDA, based on theCoq proof assistant
Mathieu, Claire. "Comparaison de modèles combinatoires et probabilistes : deux exemples en analyse d'algorithmes." Paris 11, 1988. http://www.theses.fr/1988PA112042.
Повний текст джерелаDurand, Marianne. "Combinatoire analytique et algorithmique des ensembles de données." Phd thesis, Ecole Polytechnique X, 2004. http://pastel.archives-ouvertes.fr/pastel-00000810.
Повний текст джерелаMathieu, Claire. "Comparaison de modèles combinatoires et probabilistes deux exemples en analyse d'algorithmes /." Grenoble 2 : ANRT, 1988. http://catalogue.bnf.fr/ark:/12148/cb376160528.
Повний текст джерелаHachemi, Aïcha. "Analyse Dynamique d'Algorithmes Euclidiens et Théorèmes Limites." Phd thesis, Université Paris-Diderot - Paris VII, 2007. http://tel.archives-ouvertes.fr/tel-00343537.
Повний текст джерелаLe dernier chapitre est consacré aux démonstrations de téorèmes de la limite locale. Le premier théorème est sans vitesse de convergence et concerne tous les coùts non-réseau ayant des moments forts à l'ordre trois. La condition diphantienne nous permet ensuite d'établir un théorème de la limite locale avec contrôle de la vitesse de convergence. Pour des observables suffisament régulières, nous obtenons une vitesse de convergence optimale.
Laruelle, Sophie. "Analyse d'Algorithmes Stochastiques Appliqués à la Finance." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2011. http://tel.archives-ouvertes.fr/tel-00652128.
Повний текст джерелаDe, Félice Sven. "Automates codéterministes et automates acycliques : analyse d'algorithmes et génération aléatoire." Thesis, Paris Est, 2014. http://www.theses.fr/2014PEST1111/document.
Повний текст джерелаThe general context of this thesis is the quantitative analysis of objects coming from rational language theory. We adapt techniques from the field of analysis of algorithms (average-case complexity, generic complexity, random generation...) to objects and algorithms that involve particular classes of automata. In a first part we study the complexity of Brzozowski's minimisation algorithm. Although the worst-case complexity of this algorithm is bad, it is known to be efficient in practice. Using typical properties of random mappings and random permutations, we show that the generic complexityof Brzozowski's algorithm grows faster than any polynomial in n, where n is the number of states of the automaton. In a second part, we study the random generation of acyclic automata. These automata recognize the finite sets of words, and for this reason they are widely use in applications, especially in natural language processing. We present two random generators, one using a model of Markov chain, the other a ``recursive method", based on a cominatorics decomposition of structures. The first method can be applied in many situations cases but is very difficult to calibrate, the second method is more efficient. Once implemented, this second method allows to observe typical properties of acyclic automata of large size
Boulin, Alexis. "Partitionnement des variables de séries temporelles multivariées selon la dépendance de leurs extrêmes." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ5039.
Повний текст джерелаIn a wide range of applications, from climate science to finance, extreme events with a non-negligible probability can occur, leading to disastrous consequences. Extremes in climatic events such as wind, temperature, and precipitation can profoundly impact humans and ecosystems, resulting in events like floods, landslides, or heatwaves. When the focus is on studying variables measured over time at numerous specific locations, such as the previously mentioned variables, partitioning these variables becomes essential to summarize and visualize spatial trends, which is crucial in the study of extreme events. This thesis explores several models and methods for partitioning the variables of a multivariate stationary process, focusing on extreme dependencies.Chapter 1 introduces the concepts of modeling dependence through copulas, which are fundamental for extreme dependence. The notion of regular variation, essential for studying extremes, is introduced, and weakly dependent processes are discussed. Partitioning is examined through the paradigms of separation-proximity and model-based clustering. Non-asymptotic analysis is also addressed to evaluate our methods in fixed dimensions.Chapter 2 study the dependence between maximum values is crucial for risk analysis. Using the extreme value copula function and the madogram, this chapter focuses on non-parametric estimation with missing data. A functional central limit theorem is established, demonstrating the convergence of the madogram to a tight Gaussian process. Formulas for asymptotic variance are presented, illustrated by a numerical study.Chapter 3 proposes asymptotically independent block (AI-block) models for partitioning variables, defining clusters based on the independence of maxima. An algorithm is introduced to recover clusters without specifying their number in advance. Theoretical efficiency of the algorithm is demonstrated, and a data-driven parameter selection method is proposed. The method is applied to neuroscience and environmental data, showcasing its potential.Chapter 4 adapts partitioning techniques to analyze composite extreme events in European climate data. Sub-regions with dependencies in extreme precipitation and wind speed are identified using ERA5 data from 1979 to 2022. The obtained clusters are spatially concentrated, offering a deep understanding of the regional distribution of extremes. The proposed methods efficiently reduce data size while extracting critical information on extreme events.Chapter 5 proposes a new estimation method for matrices in a latent factor linear model, where each component of a random vector is expressed by a linear equation with factors and noise. Unlike classical approaches based on joint normality, we assume factors are distributed according to standard Fréchet distributions, allowing a better description of extreme dependence. An estimation method is proposed, ensuring a unique solution under certain conditions. An adaptive upper bound for the estimator is provided, adaptable to dimension and the number of factors
Arrar, Nawel. "Problèmes de convergence, optimisation d'algorithmes et analyse stochastique de systèmes de files d'attente avec rappels." Phd thesis, Université Panthéon-Sorbonne - Paris I, 2012. http://tel.archives-ouvertes.fr/tel-00829089.
Повний текст джерелаGiroire, Frédéric. "Réseaux, algorithmique et analyse combinatoire de grands ensembles." Paris 6, 2006. http://www.theses.fr/2006PA066530.
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