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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.
The first book in inference for stochastic processes from a statistical, rather than a probabilistic, perspective. It provides a systematic exposition of theoretical results from over ten years of mathematical literature and presents, for the first time in book form, many new techniques and approaches.
Cet ouvrage constitue le troisième tome de la collection intitulée " Mathématiques appliquées ". Cette collection est résolument tournée vers les applications des mathématiques aux sciences. Elle privilégie, à cet effet, la compréhension des outils mathématiques et les principes de leur mise en œuvre dans la résolution de problèmes réels. Ce volume est consacré principalement aux premières notions de probabilité et à ses applications aux statistiques inférentielles. Il peut facilement être accessible aux étudiants de niveau " Bac + 1 ", sous réserve de connaître les notions élémentaires d'analyse mathématique, notions présentes dans la plupart des programmes des premiers cycles de l'enseignement supérieur, des classes préparatoires, du CNAM, des sections de Techniciens Supérieurs, des sections d'IUT. Le cours, assez détaillé, amorcé souvent par des exemples simples et concrets, dégage les outils et les méthodes de base, sans privilégier une trop grande technicité mathématique. Les applications technologiques ou tertiaires ont été choisies de manière à balayer un large spectre de disciplines intervenant dans bon nombre de domaines de l'industrie, de la gestion, du commerce. L'assimilation du cours est facilitée par des " Travaux Pratiques ", problèmes résolus de façon très détaillée et contenant soit des exercices de mise en œuvre de notions exposées dans le cours, soit, dans le cadre d'applications technologiques industrielles ou tertiaires, des présentations de situations réelles rencontrées couramment dans la pratique.
This book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible.
With recent studies using genetic, epigenetic, and other molecular and neurochemical approaches, a new era has begun in understanding pathophysiology of suicide. Emerging evidence suggests that neurobiological factors are not only critical in providing potential risk factors but also provide a promising approach to develop more effective treatment and prevention strategies. The Neurobiological Basis of Suicide discusses the most recent findings in suicide neurobiology. Psychological, psychosocial, and cultural factors are important in determining the risk factors for suicide; however, they offer weak prediction and can be of little clinical use. Interestingly, cognitive characteristics are different among depressed suicidal and depressed nonsuicidal subjects, and could be involved in the development of suicidal behavior. The characterization of the neurobiological basis of suicide is in delineating the risk factors associated with suicide. The Neurobiological Basis of Suicide focuses on how and why these neurobiological factors are crucial in the pathogenic mechanisms of suicidal behavior and how these findings can be transformed into potential therapeutic applications.
This is the first book to deal exclusively with oculomotor performance in a series that, up to this point, has been devoted largely to reviews of research on oculomotor anatomy and physiology. The publication of this volume signifies the recognition of the fact that a genuine understanding of eye movement will not come about through the study of neurons alone. We need to know about the capacity to make different kinds of eye movement, and about how such oculomotor capacity is used to accomplish different sorts of visual and cognitive tasks, i.e. how the eye can move and why it moves in particular ways. Research on respective topics was critically evaluated by the contributing authors, resulting in a controversial and up-to-date appraisal of this subject.
As Aristotle stated, scientific explanation is based on deductive argument-yet, Wesley C. Salmon points out, not all deductive arguments are qualified explanations. The validity of the explanation must itself be examined. Four Decades of Scientific Explanation provides a comprehensive account of the developments in scientific explanation that transpired in the last four decades of the twentieth century. It continues to stand as the most comprehensive treatment of the writings on the subject during these years.Building on the historic 1948 essay by Carl G. Hempel and Paul Oppenheim, "Studies in the Logic of Explanation," which introduced the deductive-nomological (D-N) model on which most work on scientific explanation was based for the following four decades, Salmon goes beyond this model's inherent basis of describing empirical knowledge to tells us "not only what, but also why." Salmon examines the predominant models in chronological order and describes their development, refinement, and criticism or rejection.Four Decades of Scientific Explanation underscores the need for a consensus of approach and ongoing evaluations of methodology in scientific explanation, with the goal of providing a better understanding of natural phenomena.
This lively collection of essays examines statistical ideas with an ironic eye for their essence and what their history can tell us for current disputes. The topics range from 17th-century medicine and the circulation of blood, to the cause of the Great Depression, to the determinations of the shape of the Earth and the speed of light.
The concept of the case is a basic feature of social science research and yet many questions about how a case should be defined, selected, and judged are far from settled. The contributors to this volume probe the nature of the case and the ways in which different understandings of the concept affect the conduct and the results of research. The contributions demonstrate that the work of any given researcher is often characterised by some hybrid of these basic approaches, and it is important to understand that most research involves multiple definitions and uses of cases, as both specific empirical phenomena and as general theoretical categories.