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La théorie des probabilités concerne la modélisation du hasard et le calcul des probabilités, son évaluation. La statistique fournit des outils pour la caractérisation du hasard à partir de son observation et constitue un outil incontournable d'aide à la décision. Ce livre présente la théorie des probabilités et de la statistique généralement enseignée aux ingénieurs. Tout en consacrant plus d'espace aux probabilités, il contient tous les sujets essentiels de la statistique. Il comporte trois parties : la première est une introduction à la théorie des probabilités, la deuxième partie est consacrée à l'étude des processus de Markov à temps discret et continu et aux systèmes de files d'attente, la troisième partie aborde des sujets d'usage courant de la statistique inférentielle : l'estimation, la théorie des tests et la régression linéaire. L'accent est mis sur les applications des résultats théoriques. Des exercices corrigés extraits de divers champs d'application et des programmes de simulation accompagnent chaque chapitre de l'ouvrage. Les algorithmes de simulation sont traduits en langage MATLAB en vertu de la simplicité de la syntaxe de ce dernier et de son accessibilité à bon nombre de scientifiques. Les fonctions prédéfinies dans les boîtes à outils accompagnant le logiciel MATLAB ne sont pas systématiquement utilisées afin de permettre au lecteur de traduire les programmes proposés dans n'importe quel autre langage. Ce manuel s'adresse principalement aux étudiants en génie et en sciences appliquées. Il intéresse également les enseignants, les chercheurs, les ingénieurs (génie logiciel, télécommunication, maintenance, finance) et constitue un support de cours dans les écoles d'ingénieurs et les universités.
A toddler striving to be perfect learns to accept that everyone makes mistakes.
This fourth edition contains several additions. The main ones con cern three closely related topics: Brownian motion, functional limit distributions, and random walks. Besides the power and ingenuity of their methods and the depth and beauty of their results, their importance is fast growing in Analysis as well as in theoretical and applied Proba bility. These additions increased the book to an unwieldy size and it had to be split into two volumes. About half of the first volume is devoted to an elementary introduc tion, then to mathematical foundations and basic probability concepts and tools. The second half is devoted to a detailed study of Independ ence which played and continues to playa central role both by itself and as a catalyst. The main additions consist of a section on convergence of probabilities on metric spaces and a chapter whose first section on domains of attrac tion completes the study of the Central limit problem, while the second one is devoted to random walks. About a third of the second volume is devoted to conditioning and properties of sequences of various types of dependence. The other two thirds are devoted to random functions; the last Part on Elements of random analysis is more sophisticated. The main addition consists of a chapter on Brownian motion and limit distributions.
The 36th Sminaire de Probabilits contains an advanced course on Logarithmic Sobolev Inequalities by A. Guionnet and B. Zegarlinski, as well as two shorter surveys by L. Pastur and N. O'Connell on the theory of random matrices and their links with stochastic processes. The main themes of the other contributions are Logarithmic Sobolev Inequalities, Stochastic Calculus, Martingale Theory and Filtrations. Besides the traditional readership of the Sminaires, this volume will be useful to researchers in statistical mechanics and mathematical finance.
Another title in the reissued Oxford Classic Texts in the Physical Sciences series, Jeffrey's Theory of Probability, first published in 1939, was the first to develop a fundamental theory of scientific inference based on the ideas of Bayesian statistics. His ideas were way ahead of their time and it is only in the past ten years that the subject of Bayes' factors has been significantly developed and extended. Until recently the two schools of statistics (Bayesian and Frequentist) were distinctly different and set apart. Recent work (aided by increased computer power and availability) has changed all that and today's graduate students and researchers all require an understanding of Bayesian ideas. This book is their starting point.
Intended for mathematics librarians, the list allows librarians to ascertain if a seminaire has been published, which library has it, and the forms of entry under which it has been cataloged.